Journal articles on the topic 'Combination drug therapies'
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Hoeller, Christoph. "The future of combination therapies in advanced melanoma." memo - Magazine of European Medical Oncology 13, no. 3 (August 14, 2020): 309–13. http://dx.doi.org/10.1007/s12254-020-00640-x.
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Summary The combination of Cytotoxic T-Lymphozyte Antigen-4 (CTLA‑4) and Programmed death-1 (PD‑1) antibodies and the combination of BRAF and MEK inhibitors are the current clinical standards for combination immune and targeted therapy for melanoma, respectively. The success of these therapies has stimulated research into novel drug combinations for melanoma, of which a large majority are based on combination with PD‑1 or PD-Ligand 1 (PD-L1) blocking drugs. Thus, the aim is to provide an overview of the most important combination strategies in late stage clinical development and an outlook on drug combinations in early development that might enter larger clinical trials within the next few years.
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Rideout, Todd C., Scott V. Harding, Christopher P. F. Marinangeli, and Peter J. H. Jones. "Combination drug–diet therapies for dyslipidemia." Translational Research 155, no. 5 (May 2010): 220–27. http://dx.doi.org/10.1016/j.trsl.2009.12.005.
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Plana, Deborah, Adam C. Palmer, and Peter K. Sorger. "Independent Drug Action in Combination Therapy: Implications for Precision Oncology." Cancer Discovery 12, no. 3 (March 1, 2022): 606–24. http://dx.doi.org/10.1158/2159-8290.cd-21-0212.
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Abstract Combination therapies are superior to monotherapy for many cancers. This advantage was historically ascribed to the ability of combinations to address tumor heterogeneity, but synergistic interaction is now a common explanation as well as a design criterion for new combinations. We review evidence that independent drug action, described in 1961, explains the efficacy of many practice-changing combination therapies: it provides populations of patients with heterogeneous drug sensitivities multiple chances of benefit from at least one drug. Understanding response heterogeneity could reveal predictive or pharmacodynamic biomarkers for more precise use of existing drugs and realize the benefits of additivity or synergy. Significance: The model of independent drug action represents an effective means to predict the magnitude of benefit likely to be observed in new clinical trials for combination therapies. The “bet-hedging” strategy implicit in independent action suggests that individual patients often benefit from only a subset—sometimes one—of the drugs in a combination. Personalized, targeted combination therapy, consisting of agents likely to be active in a particular patient, will increase, perhaps substantially, the magnitude of therapeutic benefit. Precision approaches of this type will require a better understanding of variability in drug response and new biomarkers, which will entail preclinical research on diverse panels of cancer models rather than studying drug synergy in unusually sensitive models.
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Webster, Rachel M. "Combination therapies in oncology." Nature Reviews Drug Discovery 15, no. 2 (February 2016): 81–82. http://dx.doi.org/10.1038/nrd.2016.3.
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Blumer, Vanessa, and Muthiah Vaduganathan. "A rationale for dedicated trials of combination therapy in heart failure." European Heart Journal Supplements 24, Supplement_L (December 1, 2022): L49—L52. http://dx.doi.org/10.1093/eurheartjsupp/suac116.
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Abstract As heart failure (HF) enters a new era with high level of evidence supporting the use of individual drug therapies, we put forth a rationale for the need for dedicated investigation of the safety, tolerability, and practicalities associated with combination medical therapy. Being able to tailor therapies via combination approaches might offer a way to maximize benefits of available therapies and also facilitate compliance. The evidentiary bar to support multi-drug regimens should be raised in HF for a variety of reasons: (1) Pivotal HF randomized controlled trials (RCTs) to date have not traditionally tested and proven safety and efficacy of drug combinations, (2) HF patients have variable disease trajectories, (3) There is hesitancy by clinicians and patients to using multiple drugs and such trials may build confidence in their use, and (4) HF therapies have overlapping side effects. Similar to combination therapies being developed and tested in adjacent fields of medicine, HF care too would greatly benefit from dedicated investigations of combination treatment approaches. Personalizing precision medicine with combination therapies has the potential to further improve outcomes and facilitate optimal implementation of disease-modifying therapies in HF.
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Gilad, Yosi, Gary Gellerman, David M. Lonard, and Bert W. O’Malley. "Drug Combination in Cancer Treatment—From Cocktails to Conjugated Combinations." Cancers 13, no. 4 (February 7, 2021): 669. http://dx.doi.org/10.3390/cancers13040669.
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It is well recognized today that anticancer drugs often are most effective when used in combination. However, the establishment of chemotherapy as key modality in clinical oncology began with sporadic discoveries of chemicals that showed antiproliferative properties and which as a first attempt were used as single agents. In this review we describe the development of chemotherapy from its origins as a single drug treatment with cytotoxic agents to polydrug therapy that includes targeted drugs. We discuss the limitations of the first chemotherapeutic drugs as a motivation for the establishment of combined drug treatment as standard practice in spite of concerns about frequent severe, dose limiting toxicities. Next, we introduce the development of targeted treatment as a concept for advancement within the broader field of small-molecule drug combination therapy in cancer and its accelerating progress that was boosted by recent scientific and technological progresses. Finally, we describe an alternative strategy of drug combinations using drug-conjugates for selective delivery of cytotoxic drugs to tumor cells that potentiates future improvement of drug combinations in cancer treatment. Overall, in this review we outline the development of chemotherapy from a pharmacological perspective, from its early stages to modern concepts of using targeted therapies for combinational treatment.
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Yordanova, Anna, and Hojjat Ahmadzadehfar. "Combination Therapies with PRRT." Pharmaceuticals 14, no. 10 (September 30, 2021): 1005. http://dx.doi.org/10.3390/ph14101005.
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Peptide receptor radionuclide therapy (PRRT) is a successful targeted radionuclide therapy in neuroendocrine tumors (NETs). However, complete responses remain elusive. Combined treatments anticipate synergistic effects and thus better responses by combining ionizing radiation with other anti-tumor treatments. Furthermore, multimodal therapies often have a balanced toxicity profile. To date, few studies have evaluated the effect of combination therapies with PRRT, some of them phase I/II trials. This review will focus on several clinically tested, tailored approaches to improving the effects of PRRT. The aim is to help clinicians in the treatment planning of NETs to choose the most effective and safe treatment for each patient in the sense of personalized medicine. Current promising combination partners of PRRT are somatostatin analogues (SSAs), chemotherapy, molecular targeted treatment, liver radioembolization, and dual radionuclide PRRT (Lutetium-177-PRRT combined with Yttrium-90-PRRT).
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Azam, Faruque, and Alexei Vazquez. "Trends in Phase II Trials for Cancer Therapies." Cancers 13, no. 2 (January 7, 2021): 178. http://dx.doi.org/10.3390/cancers13020178.
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Background: Drug combinations are the standard of care in cancer treatment. Identifying effective cancer drug combinations has become more challenging because of the increasing number of drugs. However, a substantial number of cancer drugs stumble at Phase III clinical trials despite exhibiting favourable efficacy in the earlier Phase. Methods: We analysed recent Phase II cancer trials comprising 2165 response rates to uncover trends in cancer therapies and used a null model of non-interacting agents to infer synergistic and antagonistic drug combinations. We compared our latest efficacy dataset with a previous dataset to assess the progress of cancer therapy. Results: Targeted therapies reach higher response rates when used in combination with cytotoxic drugs. We identify four synergistic and 10 antagonistic combinations based on the observed and expected response rates. We demonstrate that recent targeted agents have not significantly increased the response rates. Conclusions: We conclude that either we are not making progress or response rate measured by tumour shrinkage is not a reliable surrogate endpoint for the targeted agents.
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Pettus, Kevin, Samera Sharpe, and John R. Papp. "In VitroAssessment of Dual Drug Combinations To Inhibit Growth of Neisseria gonorrhoeae." Antimicrobial Agents and Chemotherapy 59, no. 4 (January 26, 2015): 2443–45. http://dx.doi.org/10.1128/aac.04127-14.
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ABSTRACTThe development of resistance to first-line antimicrobial therapies has led to recommendations for combination therapies for the treatment of gonorrhea infection. Recent studies have shown the success of combination therapies in treating patients, but few have reported on thein vitroactivities of these drug combinations. Anin vitroassessment of azithromycin in combination with gentamicin demonstrated inhibition of growth and suggests that clinical trials may be warranted to assess the utility of this combination in treating gonorrhea infections.
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Hwangbo, Haeun, and Adam C. Palmer. "Abstract 2739: Defining and evaluating drug additivity in clinical trials of combination cancer therapy." Cancer Research 82, no. 12_Supplement (June 15, 2022): 2739. http://dx.doi.org/10.1158/1538-7445.am2022-2739.
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Abstract BACKGROUND The benefits of combination therapy are often attributed to synergy, that is, drug interactions resulting in an anti-cancer effect that is more than the sum of its parts. Accordingly, the rationale for designing new drug combinations is often based on synergy measured in preclinical models. However, preclinical metrics of drug interaction are not applicable to clinical trial data, and there has been no established quantitative method to assess synergy versus additivity in clinical settings. We recently showed that because of extensive patient-to-patient heterogeneity in single drug responsiveness, increasing the chance of a good response to at least one drug was a quantitatively sufficient explanation for the clinical efficacy of many approved combination therapies. Some combinations surpass this ‘highest single-agent’ model, which could be due to either drug additivity or synergy. Here we propose and test a model of drug additivity for Progression-Free Survival (PFS) data from clinical trials, to identify if any approved combinations are clinically synergistic, as compared to additive. METHODS We used PFS from trials as the clinical measure of drug efficacy. We defined ‘clinical drug additivity’ as the sum of PFS times observed from each drug in a combination. Inter-patient heterogeneity in drug responses was simulated by sampling from the joint distribution of drugs’ PFS distributions. For each combination, the clinically observed PFS distribution was compared to the additivity model, or the highest single-agent model (Palmer & Sorger, 2017). Synergy is exhibited if an observed PFS distribution is significantly superior to PFS expected from additivity (by Cox Proportional Hazards). To search for drug synergy in clinical data, we analyzed approved combination therapies where synergy was most likely to explain efficacy, which are combinations where part of the regimen is not approved as monotherapy in the same disease. We analyzed 11 approved combination therapies for advanced cancers in the breast, ovary, pancreas, colon, cervix, and lymphatic system. RESULTS None of the 11 approved combinations analyzed were significantly superior to the model of clinical additivity. For five combinations, the additivity model made the most accurate predictions of clinical efficacy (mean R2=0.97 for additivity, versus R2=0.83 for highest single-agent), and the other six combinations were most accurately described by the highest single-agent model (mean R2=0.97 for highest single-agent, versus R2=0.91 for additivity). CONCLUSIONS Approved combination therapies are rarely ‘more than the sum of their parts’ in quantitative terms. A straightforward definition of clinical drug additivity accurately matched trial results for combination therapies where synergy was expected. This suggests that single-agent efficacy by each drug is usually required for the clinical success of combination therapy. Citation Format: Haeun Hwangbo, Adam C. Palmer. Defining and evaluating drug additivity in clinical trials of combination cancer therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2739.
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Conway, Brian, and Bluma G. Brenner. "Can simplified antiretroviral drug combination therapies resist resistance?" AIDS 36, no. 11 (September 1, 2022): 1597–98. http://dx.doi.org/10.1097/qad.0000000000003308.
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Henry, Mitchell L., Victor D. Bowers, Bruce G. Sommer, and Ronald M. Ferguson. "Combination drug therapies for immunosuppression in renal transplantation." Transplantation Reviews 2 (January 1988): 55–76. http://dx.doi.org/10.1016/s0955-470x(88)80006-5.
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Vakil, Vahideh, and Wade Trappe. "Drug Combinations: Mathematical Modeling and Networking Methods." Pharmaceutics 11, no. 5 (May 2, 2019): 208. http://dx.doi.org/10.3390/pharmaceutics11050208.
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Treatments consisting of mixtures of pharmacological agents have been shown to have superior effects to treatments involving single compounds. Given the vast amount of possible combinations involving multiple drugs and the restrictions in time and resources required to test all such combinations in vitro, mathematical methods are essential to model the interactive behavior of the drug mixture and the target, ultimately allowing one to better predict the outcome of the combination. In this review, we investigate various mathematical methods that model combination therapies. This survey includes the methods that focus on predicting the outcome of drug combinations with respect to synergism and antagonism, as well as the methods that explore the dynamics of combination therapy and its role in combating drug resistance. This comprehensive investigation of the mathematical methods includes models that employ pharmacodynamics equations, those that rely on signaling and how the underlying chemical networks are affected by the topological structure of the target proteins, and models that are based on stochastic models for evolutionary dynamics. Additionally, this article reviews computational methods including mathematical algorithms, machine learning, and search algorithms that can identify promising combinations of drug compounds. A description of existing data and software resources is provided that can support investigations in drug combination therapies. Finally, the article concludes with a summary of future directions for investigation by the research community.
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Thompson, Zachary J., Jamie K. Teer, Jiannong Li, Zhihua Chen, Eric A. Welsh, Yonghong Zhang, Noura Ayoubi, et al. "Drepmel—A Multi-Omics Melanoma Drug Repurposing Resource for Prioritizing Drug Combinations and Understanding Tumor Microenvironment." Cells 11, no. 18 (September 16, 2022): 2894. http://dx.doi.org/10.3390/cells11182894.
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Although substantial progress has been made in treating patients with advanced melanoma with targeted and immuno-therapies, de novo and acquired resistance is commonplace. After treatment failure, therapeutic options are very limited and novel strategies are urgently needed. Combination therapies are often more effective than single agents and are now widely used in clinical practice. Thus, there is a strong need for a comprehensive computational resource to define rational combination therapies. We developed a Shiny app, DRepMel to provide rational combination treatment predictions for melanoma patients from seventy-three thousand combinations based on a multi-omics drug repurposing computational approach using whole exome sequencing and RNA-seq data in bulk samples from two independent patient cohorts. DRepMel provides robust predictions as a resource and also identifies potential treatment effects on the tumor microenvironment (TME) using single-cell RNA-seq data from melanoma patients. Availability: DRepMel is accessible online.
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Leary, Meghan, Sarah Heerboth, Karolina Lapinska, and Sibaji Sarkar. "Sensitization of Drug Resistant Cancer Cells: A Matter of Combination Therapy." Cancers 10, no. 12 (December 4, 2018): 483. http://dx.doi.org/10.3390/cancers10120483.
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Cancer drug resistance is an enormous problem. It is responsible for most relapses in cancer patients following apparent remission after successful therapy. Understanding cancer relapse requires an understanding of the processes underlying cancer drug resistance. This article discusses the causes of cancer drug resistance, the current combination therapies, and the problems with the combination therapies. The rational design of combination therapy is warranted to improve the efficacy. These processes must be addressed by finding ways to sensitize the drug-resistant cancers cells to chemotherapy, and to prevent formation of drug resistant cancer cells. It is also necessary to prevent the formation of cancer progenitor cells by epigenetic mechanisms, as cancer progenitor cells are insensitive to standard therapies. In this article, we emphasize the role for the rational development of combination therapy, including epigenetic drugs, in achieving these goals.
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Korkut, Anil, Xubin Li, Elizabeth Kong, Gonghong Yan, Zeynep Dereli, Behnaz Bozorgui, Parisa Imanirad, et al. "Abstract LB119: Precision combination therapies based on recurrent oncogenic co-alterations." Cancer Research 82, no. 12_Supplement (June 15, 2022): LB119. http://dx.doi.org/10.1158/1538-7445.am2022-lb119.
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Abstract Cancer cells depend on multiple driver alterations whose oncogenic effects can be suppressed by drug combinations. Discovering effective combination therapies and selecting patients to maximize therapeutic benefit are challenging due to the complexity of the molecular landscape of drug responses. Here, we provide a comprehensive resource of precision combination therapies tailored to oncogenic co-alterations that are recurrent across patient cohorts. To generate the resource, we developed REcurrent Features Leveraged for Combination Therapy (REFLECT), which integrates machine learning and cancer informatics algorithms. Based on multi-omic data, the method maps recurrent co-alteration signatures in patient cohorts to combination therapies. The resource matches > 2,000 drug combinations to co-alteration signatures across 201 cohorts stratified from 10,392 patients and 33 cancer types. We validated the REFLECT pipeline using data from patient-derived xenografts (PDX), in vitro drug screens, and combination therapy clinical trials. These validations demonstrate that REFLECT selects combination therapies with significantly improved efficacy, drug synergy, and survival outcomes. In patient cohorts with immunotherapy response markers, HER2 activation, and DNA repair aberrations, we have identified therapeutically actionable and recurrent co-alteration signatures. REFLECT provides a resource and framework to design combination therapies tailored to tumor cohorts in data-driven clinical trials and pre-clinical studies. Citation Format: Anil Korkut, Xubin Li, Elizabeth Kong, Gonghong Yan, Zeynep Dereli, Behnaz Bozorgui, Parisa Imanirad, Jacob Elnaggar, Augustin Luna, David Menter, Patrick Pilié, Timothy Yap, Scott Kopetz, Chris Sander. Precision combination therapies based on recurrent oncogenic co-alterations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB119.
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Xiao, Xiao, James Trevor Oswald, Ting Wang, Weina Zhang, and Wenliang Li. "Use of Anticancer Platinum Compounds in Combination Therapies and Challenges in Drug Delivery." Current Medicinal Chemistry 27, no. 18 (June 3, 2020): 3055–78. http://dx.doi.org/10.2174/0929867325666181105115849.
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As one of the leading and most important metal-based drugs, platinum-based pharmaceuticals are widely used in the treatment of solid malignancies. Despite significant side effects and acquired drug resistance have limited their clinical applications, platinum has shown strong inhibitory effects for a wide assortment of tumors. Drug delivery systems using emerging technologies such as liposomes, dendrimers, polymers, nanotubes and other nanocompositions, all show promise for the safe delivery of platinum-based compounds. Due to the specificity of nano-formulations; unwanted side-effects and drug resistance can be largely averted. In addition, combinational therapy has been shown to be an effective way to improve the efficacy of platinum based anti-tumor drugs. This review first introduces drug delivery systems used for platinum and combinational therapeutic delivery. Then we highlight some of the recent advances in the field of drug delivery for combinational therapy; specifically progress in leveraging the cytotoxic nature of platinum-based drugs, the combinational effect of other drugs with platinum, while evaluating the drug targeting, side effect reducing and sitespecific nature of nanotechnology-based delivery platforms.
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Narayan, Ravi, Piet Molenaar, Fleur Cornelissen, Tom Wurdinger, Jan Koster, and Bart Westerman. "COMP-09. A CANCER DRUG ATLAS ENABLES PREDICTION OF PARALLEL DRUG VULNERABILITIES OF GLIOBLASTOMA." Neuro-Oncology 21, Supplement_6 (November 2019): vi62—vi63. http://dx.doi.org/10.1093/neuonc/noz175.252.
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Abstract Personalized cancer treatments using synergistic combinations of drugs is attractive but proves to be highly challenging. The combinatorial nature of such problems results in an enormous parameter space that cannot be resolved by empirical research, i.e. testing all combinations for all molecularly defined tumors. In addition, effective drug synergy is hard to predict. Here we present an approach to map data of drug-response encyclopedias and represent these as a drug atlas. This atlas consists of a framework of chemotherapeutic responses that represents a drug vulnerability landscape of cancer. Based on data from the literature we found that many synergistic drug combinations show distinct inter therapy responses and drug sensitivities. We confirmed this by performing a drug combination screen against glioblastoma where we used 270 combination experiments. From the identified dual therapies we were able to predict and validate a triple drug synergy which was validated in vivo. This new and generalizable strategy opens the door to unforeseen personalized multidrug combination approaches.
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Pontón, Iris, Andrea Martí del Rio, Marta Gómez Gómez, and David Sánchez-García. "Preparation and Applications of Organo-Silica Hybrid Mesoporous Silica Nanoparticles for the Co-Delivery of Drugs and Nucleic Acids." Nanomaterials 10, no. 12 (December 9, 2020): 2466. http://dx.doi.org/10.3390/nano10122466.
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Combination therapies rely on the administration of more than one drug, with independent mechanisms of action, aiming to enhance the efficiency of the treatment. For an optimal performance, the implementation of such therapies requires the delivery of the correct combination of drugs to a specific cellular target. In this context, the use of nanoparticles (NP) as platforms for the co-delivery of multiple drugs is considered a highly promising strategy. In particular, mesoporous silica nanoparticles (MSN) have emerged as versatile building blocks to devise complex drug delivery systems (DDS). This review describes the design, synthesis, and application of MSNs to the delivery of multiple drugs including nucleic acids for combination therapies.
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Acosta-Vélez, Giovanny, Chase Linsley, Timothy Zhu, Willie Wu, and Benjamin Wu. "Photocurable Bioinks for the 3D Pharming of Combination Therapies." Polymers 10, no. 12 (December 11, 2018): 1372. http://dx.doi.org/10.3390/polym10121372.
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Combination therapies mediate drug synergy to improve treatment efficacy and convenience, leading to higher levels of compliance. However, there are challenges with their manufacturing as well as reduced flexibility in dosing options. This study reports on the design and characterization of a polypill fabricated through the combination of material jetting and binder jetting for the treatment of hypertension. The drugs lisinopril and spironolactone were loaded into hydrophilic hyaluronic acid and hydrophobic poly(ethylene glycol) (PEG) photocurable bioinks, respectively, and dispensed through a piezoelectric nozzle onto a blank preform tablet composed of two attachable compartments fabricated via binder jetting 3D printing. The bioinks were photopolymerized and their mechanical properties were assessed via Instron testing. Scanning electron microscopy (SEM) was performed to indicate morphological analysis. The polypill was ensembled and drug release analysis was performed. Droplet formation of bioinks loaded with hydrophilic and hydrophobic active pharmaceutical ingredients (APIs) was achieved and subsequently polymerized after a controlled dosage was dispensed onto preform tablet compartments. High-performance liquid chromatography (HPLC) analysis showed sustained release profiles for each of the loaded compounds. This study confirms the potential of material jetting in conjunction with binder jetting techniques (powder-bed 3D printing), for the production of combination therapy oral dosage forms involving both hydrophilic and hydrophobic drugs.
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Lu, Da-Yong, En-Hong Chen, Hong-Ying Wu, Ting-Ren Lu, Bin Xu, and Jian Ding. "Anticancer Drug Combinations, How Far We can Go Through?" Anti-Cancer Agents in Medicinal Chemistry 17, no. 1 (January 2017): 21–28. http://dx.doi.org/10.2174/1871520616666160404112028.
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Many clinical cancer therapies are less effective by using one anticancer drug only due to refractory properties of cancer pathogenesis and drug resistance property in advanced cancer patients. A general consensus among clinicians is that anticancer drug cocktail might better control cancer progresses and metastasis than single drug therapeutics in clinical trials. Despite great popularity, the anticancer drug combination dogma has not been established. The complexity of drug combination dogma discovery is more than we can expect now. This article speculates possible routes we can undertake in this matter. The background knowledge of drug combination therapy presently practiced and possible future landscapes and drawbacks of cancer drug combinative therapies are highlighted.
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Pulkkinen, Otto I., Prson Gautam, Ville Mustonen, and Tero Aittokallio. "Multiobjective optimization identifies cancer-selective combination therapies." PLOS Computational Biology 16, no. 12 (December 28, 2020): e1008538. http://dx.doi.org/10.1371/journal.pcbi.1008538.
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Combinatorial therapies are required to treat patients with advanced cancers that have become resistant to monotherapies through rewiring of redundant pathways. Due to a massive number of potential drug combinations, there is a need for systematic approaches to identify safe and effective combinations for each patient, using cost-effective methods. Here, we developed an exact multiobjective optimization method for identifying pairwise or higher-order combinations that show maximal cancer-selectivity. The prioritization of patient-specific combinations is based on Pareto-optimization in the search space spanned by the therapeutic and nonselective effects of combinations. We demonstrate the performance of the method in the context of BRAF-V600E melanoma treatment, where the optimal solutions predicted a number of co-inhibition partners for vemurafenib, a selective BRAF-V600E inhibitor, approved for advanced melanoma. We experimentally validated many of the predictions in BRAF-V600E melanoma cell line, and the results suggest that one can improve selective inhibition of BRAF-V600E melanoma cells by combinatorial targeting of MAPK/ERK and other compensatory pathways using pairwise and third-order drug combinations. Our mechanism-agnostic optimization method is widely applicable to various cancer types, and it takes as input only measurements of a subset of pairwise drug combinations, without requiring target information or genomic profiles. Such data-driven approaches may become useful for functional precision oncology applications that go beyond the cancer genetic dependency paradigm to optimize cancer-selective combinatorial treatments.
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Margaryan, Hasmik, Dimitrios D. Evangelopoulos, Leticia Muraro Wildner, and Timothy D. McHugh. "Pre-Clinical Tools for Predicting Drug Efficacy in Treatment of Tuberculosis." Microorganisms 10, no. 3 (February 26, 2022): 514. http://dx.doi.org/10.3390/microorganisms10030514.
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Combination therapy has, to some extent, been successful in limiting the emergence of drug-resistant tuberculosis. Drug combinations achieve this advantage by simultaneously acting on different targets and metabolic pathways. Additionally, drug combination therapies are shown to shorten the duration of therapy for tuberculosis. As new drugs are being developed, to overcome the challenge of finding new and effective drug combinations, systems biology commonly uses approaches that analyse mycobacterial cellular processes. These approaches identify the regulatory networks, metabolic pathways, and signaling programs associated with M. tuberculosis infection and survival. Different preclinical models that assess anti-tuberculosis drug activity are available, but the combination of models that is most predictive of clinical treatment efficacy remains unclear. In this structured literature review, we appraise the options to accelerate the TB drug development pipeline through the evaluation of preclinical testing assays of drug combinations.
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Kucukosmanoglu, Asli, Silvia Scoarta, Thomas Wijnands, George Kanev, Bart Westerman, Bert Kiewit, David Noske, and Tom Wurdinger. "Abstract 6312: The adverse events atlas, towards a strategy to predict synergistic adverse events of combination therapies." Cancer Research 82, no. 12_Supplement (June 15, 2022): 6312. http://dx.doi.org/10.1158/1538-7445.am2022-6312.
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Abstract The current gold-standard of estimating adverse events of a drug are clinical trials. However, these trials fail to represent real-life practice where patients have additional diseases (comorbidities) and commonly use numerous drugs when entering the clinic. Therefore, there is a rise of interest in combinational therapies, also because effective combinations are expected to prevent therapy resistance. At this moment it is not feasible to predict the adverse events of new combination therapies due to lack of available information both from a dimensionality (i.e., number of adverse events recorded per patient) as well as from a patient-number perspective. When available, this information allows to choose combinational therapies with acceptable adverse events. In this study we developed a preliminary method to predict adverse events of drug combinations in order to select combinations with a mild adverse event profile. We used the FAERS, an FDA post-marketing adverse events registry as data source containing 15 million adverse-event records. First, we developed a method to visualize the adverse events profiles of monotherapy and combination therapy using dimension reduction to accurately represent the relation between adverse events over many patients. These adverse-event profiles are then fed to a convolutional neural network (CNN) to generate an explainable prediction-model for adverse events occurring in combination therapies. The CNN trained on monotherapy is able to learn from the data and recognize adverse event patterns. The learned pattern information, as stored in the so-called latent space, can be converted back onto the original adverse event profiles. This showed a high similarity to the original data, also for unseen combination therapy effects. Furthermore, a t-SNE analysis on the latent space of the CNN is able to separate additive and synergistic adverse event patterns in combination therapy. Our CNN model can successfully learn complex adverse-event patterns for single drugs and their combinations, which are all encoded in the latent space. The developed method is therefore applicable to determine the combinatorial effects of highly complex adverse event profiles. Citation Format: Asli Kucukosmanoglu, Silvia Scoarta, Thomas Wijnands, George Kanev, Bart Westerman, Bert Kiewit, David Noske, Tom Wurdinger. The adverse events atlas, towards a strategy to predict synergistic adverse events of combination therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6312.
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Du, Jian, and Xiaoying Li. "A Knowledge Graph of Combined Drug Therapies Using Semantic Predications From Biomedical Literature: Algorithm Development." JMIR Medical Informatics 8, no. 4 (April 28, 2020): e18323. http://dx.doi.org/10.2196/18323.
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Background Combination therapy plays an important role in the effective treatment of malignant neoplasms and precision medicine. Numerous clinical studies have been carried out to investigate combination drug therapies. Automated knowledge discovery of these combinations and their graphic representation in knowledge graphs will enable pattern recognition and identification of drug combinations used to treat a specific type of cancer, improve drug efficacy and treatment of human disorders. Objective This paper aims to develop an automated, visual approach to discover knowledge about combination therapies from biomedical literature, especially from those studies with high-level evidence such as clinical trial reports and clinical practice guidelines. Methods Based on semantic predications, which consist of a triple structure of subject-predicate-object (SPO), we proposed an automated algorithm to discover knowledge of combination drug therapies using the following rules: 1) two or more semantic predications (S1-P-O and Si-P-O, i = 2, 3…) can be extracted from one conclusive claim (sentence) in the abstract of a given publication, and 2) these predications have an identical predicate (that closely relates to human disease treatment, eg, “treat”) and object (eg, disease name) but different subjects (eg, drug names). A customized knowledge graph organizes and visualizes these combinations, improving the traditional semantic triples. After automatic filtering of broad concepts such as “pharmacologic actions” and generic disease names, a set of combination drug therapies were identified and characterized through manual interpretation. Results We retrieved 22,263 clinical trial reports and 31 clinical practice guidelines from PubMed abstracts by searching “antineoplastic agents” for drug restriction (published between Jan 2009 and Oct 2019). There were 15,603 conclusive claims locally parsed using the search terms “conclusion*” and “conclude*” ready for semantic predications extraction by SemRep, and 325 candidate groups of semantic predications about combined medications were automatically discovered within 316 conclusive claims. Based on manual analysis, we determined that 255/316 claims (78.46%) were accurately identified as describing combination therapies and adopted these to construct the customized knowledge graph. We also identified two categories (and 4 subcategories) to characterize the inaccurate results: limitations of SemRep and limitations of proposal. We further learned the predominant patterns of drug combinations based on mechanism of action for new combined medication studies and discovered 4 obvious markers (“combin*,” “coadministration,” “co-administered,” and “regimen”) to identify potential combination therapies to enable development of a machine learning algorithm. Conclusions Semantic predications from conclusive claims in the biomedical literature can be used to support automated knowledge discovery and knowledge graph construction for combination therapies. A machine learning approach is warranted to take full advantage of the identified markers and other contextual features.
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Li, Xinran, Angel S. N. Ng, Victor C. Y. Mak, Karen K. L. Chan, Annie N. Y. Cheung, and Lydia W. T. Cheung. "Strategic Combination Therapies for Ovarian Cancer." Current Cancer Drug Targets 20, no. 8 (September 4, 2020): 573–85. http://dx.doi.org/10.2174/1568009620666200511084007.
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Ovarian cancer remains the leading cause of gynecologic cancer-related deaths among women worldwide. The dismal survival rate is partially due to recurrence after standardized debulking surgery and first-line chemotherapy. In recent years, targeted therapies, including antiangiogenic agents or poly (ADP-ribose) polymerase inhibitors, represent breakthroughs in the treatment of ovarian cancer. As more therapeutic agents become available supplemented by a deeper understanding of ovarian cancer biology, a range of combination treatment approaches are being actively investigated to further improve the clinical outcomes of the disease. These combinations, which involve DNA-damaging agents, targeted therapies of signaling pathways and immunotherapies, simultaneously target multiple cancer pathways or hallmarks to induce additive or synergistic antitumor activities. Here we review the preclinical data and ongoing clinical trials for developing effective combination therapies in treating ovarian cancer. These emerging therapeutic modalities may reshape the treatment landscape of the disease.
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Martinelli, A., R. Moreira, and P. Cravo. "Malaria Combination Therapies: Advantages and Shortcomings." Mini-Reviews in Medicinal Chemistry 8, no. 3 (March 1, 2008): 201–12. http://dx.doi.org/10.2174/138955708783744092.
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Mrowka, Piotr, and Eliza Glodkowska-Mrowka. "PPARγ Agonists in Combination Cancer Therapies." Current Cancer Drug Targets 20, no. 3 (March 19, 2020): 197–215. http://dx.doi.org/10.2174/1568009619666191209102015.
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: Peroxisome proliferator-activated receptor-gamma (PPARγ) is a nuclear receptor acting as a transcription factor involved in the regulation of energy metabolism, cell cycle, cell differentiation, and apoptosis. These unique properties constitute a strong therapeutic potential that place PPARγ agonists as one of the most interesting and widely studied anticancer molecules. : Although PPARγ agonists exert significant, antiproliferative and tumoricidal activity in vitro, their anticancer efficacy in animal models is ambiguous, and their effectiveness in clinical trials in monotherapy is unsatisfactory. However, due to pleiotropic effects of PPARγ activation in normal and tumor cells, PPARγ ligands interact with many antitumor treatment modalities and synergistically potentiate their effectiveness. The most spectacular example is a combination of PPARγ ligands with tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML). In this setting, PPARγ activation sensitizes leukemic stem cells, resistant to any previous form of treatment, to targeted therapy. Thus, this combination is believed to be the first pharmacological therapy able to cure CML patients. : Within the last decade, a significant body of data confirming the benefits of the addition of PPARγ ligands to various antitumor therapies, including chemotherapy, hormonotherapy, targeted therapy, and immunotherapy, has been published. Although the majority of these studies have been carried out in vitro or animal tumor models, a few successful attempts to introduce PPARγ ligands into anticancer therapy in humans have been recently made. In this review, we aim to summarize shines and shadows of targeting PPARγ in antitumor therapies.
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Carter, Monique, and Saima Khan. "Novel–novel fixed-dose combination therapies." Nature Reviews Drug Discovery 18, no. 6 (April 17, 2019): 413. http://dx.doi.org/10.1038/d41573-019-00066-z.
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Jin, Wengong, Jonathan M. Stokes, Richard T. Eastman, Zina Itkin, Alexey V. Zakharov, James J. Collins, Tommi S. Jaakkola, and Regina Barzilay. "Deep learning identifies synergistic drug combinations for treating COVID-19." Proceedings of the National Academy of Sciences 118, no. 39 (September 15, 2021): e2105070118. http://dx.doi.org/10.1073/pnas.2105070118.
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Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug−target interaction and drug−drug synergy. The model consists of two parts: a drug−target interaction module and a target−disease association module. This design enables the model to utilize drug−target interaction data and single-agent antiviral activity data, in addition to available drug−drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical−chemical combination data exists.
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Vandamme, A.-M., K. Van Vaerenbergh, and E. De Clercq. "Anti-Human Immunodeficiency Virus Drug Combination Strategies." Antiviral Chemistry and Chemotherapy 9, no. 3 (June 1998): 187–203. http://dx.doi.org/10.1177/095632029800900301.
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It is now generally accepted that mono- and bitherapy for human immunodeficiency virus type 1 (HIV-1) infection are only transiently efficient mainly due to virus drug resistance. To obtain a sustained benefit from antiviral therapy, current guidelines recommend at least triple-drug combinations, or the so-called highly active antiretroviral therapy (HAART). In some patients, HAART can be problematic, either because it is difficult to remain compliant or because previous suboptimum therapies have limited the choice of drugs. For compliant drug-naive patients, HAART should be able to offer long-term virus suppression, when changing from first- to second- to third-line HAART at drug failure. Long-term treatment might ultimately result in multi-drug resistant virus leaving few options for salvage therapy. HIV drug resistance testing to guide this salvage therapy and the development of new drugs to allow new options will therefore remain priorities in anti-HIV drug research.
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Zhang, Hongbo, Wenguo Cui, Xiangmeng Qu, Huayin Wu, Liangliang Qu, Xu Zhang, Ermei Mäkilä, et al. "Photothermal-responsive nanosized hybrid polymersome as versatile therapeutics codelivery nanovehicle for effective tumor suppression." Proceedings of the National Academy of Sciences 116, no. 16 (March 29, 2019): 7744–49. http://dx.doi.org/10.1073/pnas.1817251116.
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Effective cancer therapies often demand delivery of combinations of drugs to inhibit multidrug resistance through synergism, and the development of multifunctional nanovehicles with enhanced drug loading and delivery efficiency for combination therapy is currently a major challenge in nanotechnology. However, such combinations are more challenging to administer than single drugs and can require multipronged approaches to delivery. In addition to being stable and biodegradable, vehicles for such therapies must be compatible with both hydrophobic and hydrophilic drugs, and release drugs at sustained therapeutic levels. Here, we report synthesis of porous silicon nanoparticles conjugated with gold nanorods [composite nanoparticles (cNPs)] and encapsulate them within a hybrid polymersome using double-emulsion templates on a microfluidic chip to create a versatile nanovehicle. This nanovehicle has high loading capacities for both hydrophobic and hydrophilic drugs, and improves drug delivery efficiency by accumulating at the tumor after i.v. injection in mice. Importantly, a triple-drug combination suppresses breast tumors by 94% and 87% at total dosages of 5 and 2.5 mg/kg, respectively, through synergy. Moreover, the cNPs retain their photothermal properties, which can be used to significantly inhibit multidrug resistance upon near-infrared laser irradiation. Overall, this work shows that our nanovehicle has great potential as a drug codelivery nanoplatform for effective combination therapy that is adaptable to other cancer types and to molecular targets associated with disease progression.
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Madamsetty, Vijay Sagar, Krishnendu Pal, Shamit Kumar Dutta, Enfeng Wang, and Debabrata Mukhopadhyay. "Targeted Dual Intervention-Oriented Drug-Encapsulated (DIODE) Nanoformulations for Improved Treatment of Pancreatic Cancer." Cancers 12, no. 5 (May 8, 2020): 1189. http://dx.doi.org/10.3390/cancers12051189.
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Despite recent advancements, effective treatment for pancreatic ductal adenocarcinoma (PDAC) has remained elusive. The overall survival rate in PDAC patients has been dismally low due to resistance to standard therapies. In fact, the failure of monotherapies to provide long-term survival benefits in patients led to ascension of several combination therapies for PDAC treatment. However, these combination therapies provided modest survival improvements while increasing treatment-related adverse side effects. Hence, recent developments in drug delivery methods hold the potential for enhancing therapeutic benefits by offering cocktail drug loading and minimizing chemotherapy-associated side effects. Nanoformulations-aided deliveries of anticancer agents have been a success in recent years. Yet, improving the tumor-targeted delivery of drugs to PDAC remains a major hurdle. In the present paper, we developed several new tumor-targeted dual intervention-oriented drug-encapsulated (DIODE) liposomes. We successfully formulated liposomes loaded with gemcitabine (G), paclitaxel (P), erlotinib (E), XL-184 (c-Met inhibitor, X), and their combinations (GP, GE, and GX) and evaluated their in vitro and in vivo efficacies. Our novel DIODE liposomal formulations improved median survival in comparison with gemcitabine-loaded liposomes or vehicle. Our findings are suggestive of the importance of the targeted delivery for combination therapies in improving pancreatic cancer treatment.
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Radke, Katarzyna, Karin Hansson, Jani Saarela, Aleksandr Ianevski, Philipp Ianevski, Aurélie Baudet, Mattias Magnusson, and Daniel Bexell. "Abstract 3892: High-throughput combination screen for identifying novel therapies against high-risk neuroblastoma." Cancer Research 82, no. 12_Supplement (June 15, 2022): 3892. http://dx.doi.org/10.1158/1538-7445.am2022-3892.
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Abstract Neuroblastoma is a childhood solid tumor commonly located in the adrenal gland. High-risk neuroblastoma patients undergo multidrug and multi-interventional treatment but despite these efforts many patients develop resistance to chemotherapy and eventually relapse. Neuroblastoma patients face an urgent need of novel therapeutic strategies that could effectively combat the disease. In a recent study we identified kinesin spindle protein (KSP) as a highly effective target in high-risk neuroblastoma. Pharmacological inhibition of KSP resulted in complete regression of a subset of neuroblastoma subcutaneous PDX tumors and tumor growth delay in orthotopic PDXs (Hansson, Radke et al, Sci. Transl. Med 2020). The purpose of the present study is to identify novel synergistic drug combinations effective against neuroblastoma and investigate their translational potential. We designed, optimized, and performed a high-throughput combination screen using 3D tumor organoids that represent three MYCN-amplified neuroblastoma tumors. KSP inhibition was tested in combination with the FIMM drug set of 527 approved and investigational drugs. Screening was performed stepwise: • Step 1: High-throughput drug screening 527 drug combinations and machine learning prediction (DECREASE tool) of drug combination synergy based on minimal required input. • Step 2: Screening of 6x6 dose-response matrices of top 26 predicted hits in high-risk neuroblastoma models. • Step 3: Screening of 6x6 dose-response matrices of top 26 hits in CD34+ cord blood controls to identify compounds that have low toxicity towards proliferating fraction of hematopoietic cells. Overall, we identified seven drug candidates (P1-P7). They classify as: kinase inhibitors, differentiating modifiers, and one hormone therapy. Screened combinations were chosen based on selective efficacy (difference between anti-neuroblastoma efficacy and efficacy towards CD34+ cord blood controls). Identified drug combinations are common among three high-risk neuroblastoma models and have most synergistic area score (MSA) over five. We further plan to validate seven hits in neuroblastoma models in vitro, investigate MOA of the most promising combination and test it in PDX models in vivo. Citation Format: Katarzyna Radke, Karin Hansson, Jani Saarela, Aleksandr Ianevski, Philipp Ianevski, Aurélie Baudet, Mattias Magnusson, Daniel Bexell. High-throughput combination screen for identifying novel therapies against high-risk neuroblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3892.
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Palmer, Adam C., Benjamin Izar, Haeun Hwangbo, and Peter K. Sorger. "Predictable Clinical Benefits without Evidence of Synergy in Trials of Combination Therapies with Immune-Checkpoint Inhibitors." Clinical Cancer Research 28, no. 2 (January 15, 2022): 368–77. http://dx.doi.org/10.1158/1078-0432.ccr-21-2275.
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Abstract Purpose: Combinations of immune-checkpoint inhibitors (ICI) with other cancer therapies have been approved for advanced cancers in multiple indications, and numerous trials are under way to test new combinations. However, the mechanisms that account for the superiority of approved ICI combinations relative to their constituent monotherapies remain unknown. Experimental Design: We analyzed 13 phase III clinical trials testing combinations of ICIs with each other or other drugs in patients with advanced melanoma and lung, breast, gastric, kidney, and head and neck cancers. The clinical activity of the individual constituent therapies, measured in the same or a closely matched trial cohort, was used to compute progression-free survival (PFS) curves expected under a model of independent drug action. To identify additive or synergistic efficacy, PFS expected under this null model was compared with observed PFS by Cox regression. Results: PFS elicited by approved combination therapies with ICIs could be accurately predicted from monotherapy data using the independent drug action model (Pearson r = 0.98, P < 5 × 10−9, N = 4,173 patients, 8 types of cancer). We found no evidence of drug additivity or synergy except in one trial in which such interactions might have extended median PFS by 9 days. Conclusions: Combining ICIs with other cancer therapies affords predictable and clinically meaningful benefit by providing patients with multiple chances of response to a single agent. Conversely, there exists no evidence in phase III trials that other therapies interact with and enhance the activity of ICIs. These findings can inform the design and testing of new ICI combination therapies while emphasizing the importance of developing better predictors (biomarkers) of ICI response.
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Tiek, Deanna, and Shi-Yuan Cheng. "DNA damage and metabolic mechanisms of cancer drug resistance." Cancer Drug Resistance 5, no. 2 (2022): 368–79. http://dx.doi.org/10.20517/cdr.2021.148.
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Cancer drug resistance is one of the main barriers to overcome to ensure durable treatment responses. While many pivotal advances have been made in first combination therapies, then targeted therapies, and now broadening out to immunomodulatory drugs or metabolic targeting compounds, drug resistance is still ultimately universally fatal. In this brief review, we will discuss different strategies that have been used to fight drug resistance from synthetic lethality to tumor microenvironment modulation, focusing on the DNA damage response and tumor metabolism both within tumor cells and their surrounding microenvironment. In this way, with a better understanding of both targetable mutations in combination with the metabolism, smarter drugs may be designed to combat cancer drug resistance.
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Nsanzabana, Christian. "Resistance to Artemisinin Combination Therapies (ACTs): Do Not Forget the Partner Drug!" Tropical Medicine and Infectious Disease 4, no. 1 (February 1, 2019): 26. http://dx.doi.org/10.3390/tropicalmed4010026.
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Artemisinin-based combination therapies (ACTs) have become the mainstay for malaria treatment in almost all malaria endemic settings. Artemisinin derivatives are highly potent and fast acting antimalarials; but they have a short half-life and need to be combined with partner drugs with a longer half-life to clear the remaining parasites after a standard 3-day ACT regimen. When introduced, ACTs were highly efficacious and contributed to the steep decrease of malaria over the last decades. However, parasites with decreased susceptibility to artemisinins have emerged in the Greater Mekong Subregion (GMS), followed by ACTs’ failure, due to both decreased susceptibility to artemisinin and partner drug resistance. Therefore, there is an urgent need to strengthen and expand current resistance surveillance systems beyond the GMS to track the emergence or spread of artemisinin resistance. Great attention has been paid to the spread of artemisinin resistance over the last five years, since molecular markers of decreased susceptibility to artemisinin in the GMS have been discovered. However, resistance to partner drugs is critical, as ACTs can still be effective against parasites with decreased susceptibility to artemisinins, when the latter are combined with a highly efficacious partner drug. This review outlines the different mechanisms of resistance and molecular markers associated with resistance to partner drugs for the currently used ACTs. Strategies to improve surveillance and potential solutions to extend the useful therapeutic lifespan of the currently available malaria medicines are proposed.
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Moschetta, Michele, Marta Cesca, Francesca Pretto, and Raffaella Giavazzi. "Angiogenesis Inhibitors: Implications for Combination with Conventional Therapies." Current Pharmaceutical Design 16, no. 35 (December 1, 2010): 3921–31. http://dx.doi.org/10.2174/138161210794455021.
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van Hasselt, J. G. Coen, and Ravi Iyengar. "Systems Pharmacology: Defining the Interactions of Drug Combinations." Annual Review of Pharmacology and Toxicology 59, no. 1 (January 6, 2019): 21–40. http://dx.doi.org/10.1146/annurev-pharmtox-010818-021511.
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The majority of diseases are associated with alterations in multiple molecular pathways and complex interactions at the cellular and organ levels. Single-target monotherapies therefore have intrinsic limitations with respect to their maximum therapeutic benefits. The potential of combination drug therapies has received interest for the treatment of many diseases and is well established in some areas, such as oncology. Combination drug treatments may allow us to identify synergistic drug effects, reduce adverse drug reactions, and address variability in disease characteristics between patients. Identification of combination therapies remains challenging. We discuss current state-of-the-art systems pharmacology approaches to enable rational identification of combination therapies. These approaches, which include characterization of mechanisms of disease and drug action at a systems level, can enable understanding of drug interactions at the molecular, cellular, physiological, and organismal levels. Such multiscale understanding can enable precision medicine by promoting the rational development of combination therapy at the level of individual patients for many diseases.
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Sigaux, François. "Can CML stem cells be cleared using combination drug therapies?" Hématologie 18, no. 2 (March 2012): 83–84. http://dx.doi.org/10.1684/hma.2012.0699.
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Piretto, E., M. Delitala, and M. Ferraro. "How combination therapies shape drug resistance in heterogeneous tumoral populations." Letters in Biomathematics 5, sup1 (May 14, 2018): S160—S177. http://dx.doi.org/10.1080/23737867.2018.1465862.
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Lei, Fan, Xinyuan Xi, Surinder K. Batra, and Tatiana K. Bronich. "Combination Therapies and Drug Delivery Platforms in Combating Pancreatic Cancer." Journal of Pharmacology and Experimental Therapeutics 370, no. 3 (February 22, 2019): 682–94. http://dx.doi.org/10.1124/jpet.118.255786.
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Saputra, Elysia, and Lisa Tucker-Kellogg. "Abstract B026: Simulations of cancer evolution predict relative benefits of synergistic and non-synergistic drug combinations for combating different landscapes of drug-resistance." Cancer Research 82, no. 10_Supplement (May 15, 2022): B026. http://dx.doi.org/10.1158/1538-7445.evodyn22-b026.
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Abstract Cancer therapies often have short duration of success due to the development of drug-resistance, which motivates research into anti-evolutionary therapies. A popular strategy is to combine two drugs, particularly if they have synergistic (greater-than-additive) efficacy. However, therapies that cause synergistic suppression of drug-sensitive cells will also suffer from synergistic release of drug-resistant cells, when drug-resistance arises. Not yet understood is the impact of partial drug-resistance and semi-resistant phenotypes on the speed of clonal expansion under combination therapy. In this work, we perform computational modeling to compare the growth rates of heterogeneous populations of cells, when drug-resistance is a binary (all-or-nothing) phenotype, versus when it is a spectrum of partially-sensitive phenotypes. We ask whether the type and granularity of drug-resistance levels in the drug-resistance landscape affects the relative outcome of using synergistic versus non-synergistic drug combinations. We observe that if drug-resistance can develop via rapid, “plastic” phenotypic transitions, then synergistic drugs provide long-lasting suppression of resistance than additive drugs (when dosed to have equal initial efficacy). Conversely, if drug-resistance develops via slow acquisition of phenotypic changes (e.g., requiring multiple genomic lesions), then synergistic drugs promote faster evolution of drug-resistance than additive drugs. When studying how synergism affects evolution, we found that cells escaping a synergistic treatment would be more likely to develop “asymmetric resistance” toward one drug (with greater sensitivity toward the other drug). In contrast, cells escaping antagonistic treatments would be more likely to develop symmetric resistance (equal levels of resistance) toward both drugs. Our findings suggest that after a synergistic treatment, a post-relapse tumor would be more likely to retain sensitivity to one of the drugs used, compared with equivalent relapse after non-synergistic combination treatment. Thus, theoretical modeling can provide testable predictions that complement the hypotheses arising from experiments. Citation Format: Elysia Saputra, Lisa Tucker-Kellogg. Simulations of cancer evolution predict relative benefits of synergistic and non-synergistic drug combinations for combating different landscapes of drug-resistance [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B026.
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Fivelman, Quinton L., Ipemida S. Adagu, and David C. Warhurst. "Effects of Piperaquine, Chloroquine, and Amodiaquine on Drug Uptake and of These in Combination with Dihydroartemisinin against Drug-Sensitive and -Resistant Plasmodium falciparum Strains." Antimicrobial Agents and Chemotherapy 51, no. 6 (April 2, 2007): 2265–67. http://dx.doi.org/10.1128/aac.01479-06.
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ABSTRACT Piperaquine is being developed as a long-acting component in artemisinin combination therapies. It was highly active in vitro and drug interaction studies showed that dihydroartemisinin combinations with piperaquine, chloroquine, and amodiaquine were indifferent tending toward antagonism. Competitive uptake of radiolabeled chloroquine and dihydroartemisinin in combination with other antimalarials was observed.
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Nishimura, Kaneyasu, and Kazuyuki Takata. "Combination of Drugs and Cell Transplantation: More Beneficial Stem Cell-Based Regenerative Therapies Targeting Neurological Disorders." International Journal of Molecular Sciences 22, no. 16 (August 22, 2021): 9047. http://dx.doi.org/10.3390/ijms22169047.
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Cell transplantation therapy using pluripotent/multipotent stem cells has gained attention as a novel therapeutic strategy for treating neurodegenerative diseases, including Parkinson’s disease, Alzheimer’s disease, Huntington’s disease, ischemic stroke, and spinal cord injury. To fully realize the potential of cell transplantation therapy, new therapeutic options that increase cell engraftments must be developed, either through modifications to the grafted cells themselves or through changes in the microenvironment surrounding the grafted region. Together these developments could potentially restore lost neuronal function by better supporting grafted cells. In addition, drug administration can improve the outcome of cell transplantation therapy through better accessibility and delivery to the target region following cell transplantation. Here we introduce examples of drug repurposing approaches for more successful transplantation therapies based on preclinical experiments with clinically approved drugs. Drug repurposing is an advantageous drug development strategy because drugs that have already been clinically approved can be repurposed to treat other diseases faster and at lower cost. Therefore, drug repurposing is a reasonable approach to enhance the outcomes of cell transplantation therapies for neurological diseases. Ideal repurposing candidates would result in more efficient cell transplantation therapies and provide a new and beneficial therapeutic combination.
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Green, Adrian J., Benedict Anchang, Farida S. Akhtari, David M. Reif, and Alison Motsinger-Reif. "Extending the lymphoblastoid cell line model for drug combination pharmacogenomics." Pharmacogenomics 22, no. 9 (June 2021): 543–51. http://dx.doi.org/10.2217/pgs-2020-0160.
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Combination drug therapies have become an integral part of precision oncology, and while evidence of clinical effectiveness continues to grow, the underlying mechanisms supporting synergy are poorly understood. Immortalized human lymphoblastoid cell lines (LCLs) have been proven as a particularly useful, scalable and low-cost model in pharmacogenetics research, and are suitable for elucidating the molecular mechanisms of synergistic combination therapies. In this review, we cover the advantages of LCLs in synergy pharmacogenomics and consider recent studies providing initial evidence of the utility of LCLs in synergy research. We also discuss several opportunities for LCL-based systems to address gaps in the research through the expansion of testing regimens, assessment of new drug classes and higher-order combinations, and utilization of integrated omics technologies.
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Tsigelny, Igor F. "Artificial intelligence in drug combination therapy." Briefings in Bioinformatics 20, no. 4 (February 9, 2018): 1434–48. http://dx.doi.org/10.1093/bib/bby004.
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AbstractCurrently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many cases, one drug cannot target all necessary points of intervention. For example, in cancer therapy, a physician often meets a patient having a genomic profile including more than five molecular aberrations. Drug combination therapy has been an area of interest for a while, for example the classical work of Loewe devoted to the synergism of drugs was published in 1928—and it is still used in calculations for optimal drug combinations. More recently, over the past several years, there has been an explosion in the available information related to the properties of drugs and the biomedical parameters of patients. For the drugs, hundreds of 2D and 3D molecular descriptors for medicines are now available, while for patients, large data sets related to genetic/proteomic and metabolomics profiles of the patients are now available, as well as the more traditional data relating to the histology, history of treatments, pretreatment state of the organism, etc. Moreover, during disease progression, the genetic profile can change. Thus, the ability to optimize drug combinations for each patient is rapidly moving beyond the comprehension and capabilities of an individual physician. This is the reason, that biomedical informatics methods have been developed and one of the more promising directions in this field is the application of artificial intelligence (AI). In this review, we discuss several AI methods that have been successfully implemented in several instances of combination drug therapy from HIV, hypertension, infectious diseases to cancer. The data clearly show that the combination of rule-based expert systems with machine learning algorithms may be promising direction in this field.
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Pugh, Trevor J., and Benjamin Haibe-Kains. "REFLECTions on Combination Therapies Empowered by Data Sharing." Cancer Discovery 12, no. 6 (June 2, 2022): 1416–18. http://dx.doi.org/10.1158/2159-8290.cd-22-0330.
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Summary: Li and colleagues present REFLECT, a computational approach to precision oncology that nominates effective drug combinations by utilizing a diverse compendium of publicly available preclinical and clinical genomic, transcriptomic, and proteomic data. The preliminary validation of the REFLECT system in preclinical and clinical trial settings showcases potential for clinical implementation, although challenges remain. See related article by Li et al., p. 1542 (4).
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Verma, Nandini, Yaron Vinik, Ashish Saroha, Nishanth Ulhas Nair, Eytan Ruppin, Gordon Mills, Thomas Karn, et al. "Synthetic lethal combination targeting BET uncovered intrinsic susceptibility of TNBC to ferroptosis." Science Advances 6, no. 34 (August 2020): eaba8968. http://dx.doi.org/10.1126/sciadv.aba8968.
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Identification of targeted therapies for TNBC is an urgent medical need. Using a drug combination screen reliant on synthetic lethal interactions, we identified clinically relevant combination therapies for different TNBC subtypes. Two drug combinations targeting the BET family were further explored. The first, targeting BET and CXCR2, is specific for mesenchymal TNBC and induces apoptosis, whereas the second, targeting BET and the proteasome, is effective for major TNBC subtypes and triggers ferroptosis. Ferroptosis was induced at low drug doses and was associated with increased cellular iron and decreased glutathione levels, concomitant with reduced levels of GPX4 and key glutathione biosynthesis genes. Further functional studies, analysis of clinical datasets and breast cancer specimens revealed a unique vulnerability of TNBC to ferroptosis inducers, enrichment of ferroptosis gene signature, and differential expression of key proteins that increase labile iron and decrease glutathione levels. This study identified potent combination therapies for TNBC and unveiled ferroptosis as a promising therapeutic strategy.
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Piretto, Elena, Gianluca Selvaggio, Damiano Bragantini, Enrico Domenici, and Luca Marchetti. "A novel logical model of COVID-19 intracellular infection to support therapies development." PLOS Computational Biology 18, no. 8 (August 29, 2022): e1010443. http://dx.doi.org/10.1371/journal.pcbi.1010443.
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In this paper, a logical-based mathematical model of the cellular pathways involved in the COVID-19 infection has been developed to study various drug treatments (single or in combination), in different illness scenarios, providing insights into their mechanisms of action. Drug simulations suggest that the effects of single drugs are limited, or depending on the scenario counterproductive, whereas better results appear combining different treatments. Specifically, the combination of the anti-inflammatory Baricitinib and the anti-viral Remdesivir showed significant benefits while a stronger efficacy emerged from the triple combination of Baricitinib, Remdesivir, and the corticosteroid Dexamethasone. Together with a sensitivity analysis, we performed an analysis of the mechanisms of the drugs to reveal their impact on molecular pathways.