Journal articles: 'Drug selection'

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Podrid, P. J. "Antiarrhythmic Drug Selection." Annual Review of Medicine 38, no. 1 (February 1987): 1–17. http://dx.doi.org/10.1146/annurev.me.38.020187.000245.

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Klunker, W. "Boenninghausen's drug selection." British Homoeopathic journal 85, no. 1 (January 1996): 41. http://dx.doi.org/10.1016/s0007-0785(96)80038-x.

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Crumrine, Patricia K. "Antiepileptic Drug Selection in Pediatric Epilepsy." Journal of Child Neurology 17, no. 2_suppl (February 2002): 2S2–2S8. http://dx.doi.org/10.1177/08830738020170020701.

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This review article presents information concerning treatment options for various pediatric epilepsy syndromes. The decisions made in the selection of antiepileptic drugs are determined by a number of variables that include, but are exclusive of, risk of seizure recurrence, patient age, epilepsy syndrome, known drug reactions, and prognosis of the epilepsy syndrome. The review discusses issues pertinent to antiepileptic drug selection including simple pharmacokinetic principles, antiepileptic drug formulations, and information concerning clinical studies using some of the antiepileptic drugs. Information is provided concerning the issues of seizure recurrence. Suggested paradigms for antiepileptic drug selection for partial seizures are provided. A table of antiepileptic drug costs is provided for assistance in prescribing and advising families. Psychosocial issues pertinent to the treatment of children are discussed. (J Child Neurol 2002;17:2S2—2S8).

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Brook, Robert D. "Drug selection in hypertension." ACC Current Journal Review 13, no. 12 (December 2004): 21–26. http://dx.doi.org/10.1016/j.accreview.2004.11.003.

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Mitchell, Aaron Philip, Aaron Winn, and Stacie Dusetzina. "Pharmaceutical industry payments and oncologist drug selection." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): 6510. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.6510.

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6510 Background: Financial relationships between physicians and the pharmaceutical industry are common, and have the potential to influence clinical practice in potentially inappropriate ways. Oncology may be an ideal setting to study the influence of industry payments on physician drug choice given the high levels of competition for market share and high prices commanded by orally administered oncologic drugs. Methods: We linked the Open Payments database of industry-physician financial transactions with the Medicare Part D Prescriber file by physician name and practice location. We used McFadden’s conditional logit model to determine whether receipt of industry payments was associated with higher odds of using a drug manufactured by the same company. We applied this model to clinical scenarios in which oncologists may choose between multiple, on-patent drugs: metastatic renal cell cancer (mRCC) (sunitinib, sorafenib, and pazopanib) and chronic myeloid leukemia (CML) (imatinib, dasatinib, and nilotinib). The primary, binary independent variable was receipt of payments from a manufacturer of one of these drugs in 2013; the primary dependent variable was choosing that manufacturer’s drug in 2014. We divided industry payments into two categories, research payments and non-research “general” payments (including meals, travel, lodging, and speaking/consulting fees), and analyzed each payment type separately. Results: Physicians who received general payments from a manufacturer had increased odds of prescribing that manufacturer’s drug for both mRCC (OR: 1.78, 95%CI 1.23-2.57, mean payments $566) and CML (OR: 1.29, 95%CI 1.13-1.48, mean payments $166). Research payments were associated with an increased odds of manufacturer drug use for mRCC (OR: 2.13, 95%CI 1.13-4.00, mean payments $33,391) but not CML (OR: 1.10, 95%CI 0.83-1.45, mean payments $185,763). Conclusions: Receipt of general payments from pharmaceutical companies is associated with increased prescribing of those companies’ drugs. An association between research payments and prescribing was less consistent. This study suggests that conflicts of interest with the pharmaceutical industry may influence oncologists in high-stakes treatment decisions for patients with cancer.

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sah, Sushilkumar, Dr AjayKumar Tiwari, and Prof B. Shrivastava. "FLOATING DRUG DELIVERY SYSTEMS: RATIONALE FOR DRUG SELECTION." International Journal of Advanced Research 4, no. 10 (October 31, 2016): 127–33. http://dx.doi.org/10.21474/ijar01/1768.

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Lu, Da-Yong, and Ting-Ren Lu. "Drug sensitivity testing, a unique drug selection strategy." Advances in Biomarker Sciences and Technology 2 (2020): 59–66. http://dx.doi.org/10.1016/j.abst.2020.11.001.

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Banerjee, Indrajit. "Concepts of P Drug Selection." Nepal Journal of Epidemiology 3, no. 1 (March 30, 2013): 226–29. http://dx.doi.org/10.3126/nje.v3i1.8280.

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Personal (P) drug selection is an important part of the pharmacology teaching and learning session. Most of the textbooks that are commonly followed by the medical schools of Nepal merely tell about the concepts of P drug selection. Most of the time it is found that student cannot follow the concepts of P drug. Most of the literature that is available is in the international level, like international journals, guide to good prescription, teachers guide to good prescription etc. At the national level very few references are available. The activity of P drug selection can reduce the chances of irrational prescribing that is common problem in developing country like Nepal. Some of the important concepts regarding P Drug selection like it is a personal drug for a doctor and it is not for a patient, P drug is selected for a disease and not for a particular patient is also has been emphasized in this paper.DOI: http://dx.doi.org/10.3126/nje.v3i1.8280 Nepal Journal of Epidemiology 2013;3 (1): 226-229

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Knowles, Jonathan, and Gianni Gromo. "Target selection in drug discovery." Nature Reviews Drug Discovery 2, no. 1 (January 2003): 63–69. http://dx.doi.org/10.1038/nrd986.

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Harrigan, P. Richard, and Christopher S. Alexander. "Selection of drug-resistant HIV." Trends in Microbiology 7, no. 3 (March 1999): 120–23. http://dx.doi.org/10.1016/s0966-842x(99)01467-5.

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Christensen, Thomas P., Duane M. Kirking, Frank J. Ascione, Lynda S. Welage, and Caroline A. Gaither. "Drug Product Selection: Legal Issues." Journal of the American Pharmaceutical Association (1996) 41, no. 6 (November 2001): 868–74. http://dx.doi.org/10.1016/s1086-5802(16)31328-6.

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Cheng, Lijun, Bryan P. Schneider, and Lang Li. "A bioinformatics approach for precision medicine off-label drug drug selection among triple negative breast cancer patients." Journal of the American Medical Informatics Association 23, no. 4 (April 23, 2016): 741–49. http://dx.doi.org/10.1093/jamia/ocw004.

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ABSTRACT Background Cancer has been extensively characterized on the basis of genomics. The integration of genetic information about cancers with data on how the cancers respond to target based therapy to help to optimum cancer treatment. Objective The increasing usage of sequencing technology in cancer research and clinical practice has enormously advanced our understanding of cancer mechanisms. The cancer precision medicine is becoming a reality. Although off-label drug usage is a common practice in treating cancer, it suffers from the lack of knowledge base for proper cancer drug selections. This eminent need has become even more apparent considering the upcoming genomics data. Methods In this paper, a personalized medicine knowledge base is constructed by integrating various cancer drugs, drug-target database, and knowledge sources for the proper cancer drugs and their target selections. Based on the knowledge base, a bioinformatics approach for cancer drugs selection in precision medicine is developed. It integrates personal molecular profile data, including copy number variation, mutation, and gene expression. Results By analyzing the 85 triple negative breast cancer (TNBC) patient data in the Cancer Genome Altar, we have shown that 71.7% of the TNBC patients have FDA approved drug targets, and 51.7% of the patients have more than one drug target. Sixty-five drug targets are identified as TNBC treatment targets and 85 candidate drugs are recommended. Many existing TNBC candidate targets, such as Poly (ADP-Ribose) Polymerase 1 (PARP1), Cell division protein kinase 6 (CDK6), epidermal growth factor receptor, etc., were identified. On the other hand, we found some additional targets that are not yet fully investigated in the TNBC, such as Gamma-Glutamyl Hydrolase (GGH), Thymidylate Synthetase (TYMS), Protein Tyrosine Kinase 6 (PTK6), Topoisomerase (DNA) I, Mitochondrial (TOP1MT), Smoothened, Frizzled Class Receptor (SMO), etc. Our additional analysis of target and drug selection strategy is also fully supported by the drug screening data on TNBC cell lines in the Cancer Cell Line Encyclopedia. Conclusions The proposed bioinformatics approach lays a foundation for cancer precision medicine. It supplies much needed knowledge base for the off-label cancer drug usage in clinics.

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Putri, Awanda Octavianti, and Eka Prasetyaningrum. "Sistem Pendukung Keputusan Pemilihan Supplier Dengan Metode AHP Pada Apotek & Laboratorium Klinik Interna Berbasis Web." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 4 (October 26, 2021): 1353. http://dx.doi.org/10.30865/mib.v5i4.3236.

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Inventory of products or goods in the form of medicines and others is one of the work activities. In procuring products, in this case drugs, the selection of drug suppliers is very important for a drugstore business, pharmacy or regional hospital. Interna Pharmacy & Clinical Laboratory is a private pharmacy located in Sampit, East Kotawaringin, Central Kalimantan. The problem that exists at the Internal Pharmacy & Clinical Laboratory is the selection of suppliers by comparing several suppliers or only based on the thoughts of the owner, pharmacist or pharmacist assistant. Suppliers are selected based on price, number of discounts given, speed of delivery of goods, completeness of drugs and drug packaging. Selection of suppliers with a system that has been running with various criteria resulted in the selection of suppliers is not accurate and takes time. Based on the problems above, it is very necessary to have a decision support system website using the Analytical Hierarchy Process method. By using a decision support system with the Analytical Hierarchy Process method, the Pharmacy & Internal Clinical Laboratory can make an assessment by comparing each criterion. The output of this research is a decision support system using the Analytical Hierarchy Process method. This study aims to make it easier to select drug suppliers from various criteria, save time in selecting drug suppliers, get good suppliers for the procurement of the necessary drugs

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Faizullin, Sh B., and M. S. Musin. "Principles of rational selection and use of drug combinations." Kazan medical journal 67, no. 5 (September 15, 1986): 397–400. http://dx.doi.org/10.17816/kazmj70726.

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Advances in pharmacy, pharmacology in recent years and the rapid development of the chemical-pharmaceutical industry have significantly expanded the arsenal of drugs used, the number of which continues to increase. With the introduction into practice of new drugs, the possibilities of etiotropic and pathogenetic therapy have increased. However, at the same time as the effectiveness of drug therapy has increased, it has become less safe. In recent years, cases of drug-induced side effects, which often exceed the severity of the underlying disease, have increased.

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Banerjee, I., A. Saha, B. Sathian, B. Roy, and I. Banerjee. "Evaluation of Medical students perception on Personal Drug Selection for improving prospective Medical Education: A situational Analysis from Manipal College of Medical Sciences, Pokhara, Nepal." Nepal Journal of Medical Sciences 3, no. 2 (September 22, 2015): 89–93. http://dx.doi.org/10.3126/njms.v3i2.13450.

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Background: Personal (P) drug selection is an important part of the pharmacology teaching and learning session. Most of the textbooks that are commonly followed by the medical schools of Nepal merely tell about the concepts of P drug selection. P drug selection lets students to think and make decisions about the drugs prescribed. The main objective of the study was to find the Medical students perspective of P- Drug selection from a medical college of Nepal.Methods: This cross sectional questionnaires based study was carried out at Manipal College of Medical Sciences, Pokhara, Nepal from July 2008- July 2013. Chi square test and Odds Ratio were used for analytical purpose. Questionnaire validation tests showed that the Alpha Cronbach was 0.72.Results: P drug selection is an important part of MBBS curriculum, which was reflected by 74.9% of the medical students. Most of the students, around 82% responded that P drug is for a disease and 85% students felt that time should be increased for the exercise. Around 90.1% students felt that P drug selection gives knowledge of the full chapter and 90.1% of the students found it difficult to find out the cost of the drugs from different brands.Conclusion: The overall views of medical students on P drug selection were positive. The teaching and learning activity of P Drug Selection needs improvement in certain areas. In this exercise a student learns the rationale drug usage for a particular disease objectively and in an unbiased manner. With proper amendments in the teaching and learning methodology of P drug selection students can think and make decisions about the prescription writing, furthermore can reduce the chances of irrational prescribing by the future doctors. Nepal Journal of Medical Sciences Vol.3(2) 2014: 89-93

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Kumar, Manish, Lalit Mohan, Hitesh Mishra, Akash Chandra, and Harihar Dikshit. "Students’ attitudes on personal drug selection exercise in writing prescription." International Journal of Basic & Clinical Pharmacology 7, no. 1 (December 23, 2017): 147. http://dx.doi.org/10.18203/2319-2003.ijbcp20175691.

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Background: This study was developed to know the students’ views regarding personal drug (P-drug) concept in rational prescription of drugs and also giving them training of creating and using personal drug concept.Methods: 40 medical students (5th semester) divided in four groups were involved voluntarily in a three phase, questionnaire based and prospective study. In first and second phase students were taught and asked to derive P-drug using different standard text books and Current Index of Medical Specialties (CIMS) by analyzing efficacy, safety, cost and convenience of drugs used for type II diabetes mellitus. Third phase was designed to know the students’ perception regarding the exercise and difficulties faced in the process of P-drug selection. It contained demographic and 12 questions with answer using Likert scale.Results: Students selected biguanide (metformin) as a P-drug in terms of efficacy, safety, cost and convenience. 95% (36 out of 40) responded in the questionnaire, out of which 92% (33 out of 36) had given answer with mean score ≥4. Overall median score was 4 and Interquartile Range was 4-5. 89% (32) strongly agreed that P-dug selection teaching helped them to understand pharmacology better. Majority (83% or 30) were in favour of introducing P-drug selection exercises in undergraduate pharmacology curriculum.Conclusions: P-drug selection exercise helped students to understand the differences among various drugs used for the treatment of type II diabetes mellitus and given them a strong foundation for developing rational use of the medicine in their future career as a doctor.

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Beilstein, Sabina, Radhia El Phil, Suzanne Sahraoui, Leonardo Scapozza, Marcel Kaiser, and Pascal Mäser. "Laboratory Selection of Trypanosomatid Pathogens for Drug Resistance." Pharmaceuticals 15, no. 2 (January 24, 2022): 135. http://dx.doi.org/10.3390/ph15020135.

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The selection of parasites for drug resistance in the laboratory is an approach frequently used to investigate the mode of drug action, estimate the risk of emergence of drug resistance, or develop molecular markers for drug resistance. Here, we focused on the How rather than the Why of laboratory selection, discussing different experimental set-ups based on research examples with Trypanosoma brucei, Trypanosoma cruzi, and Leishmania spp. The trypanosomatids are particularly well-suited to illustrate different strategies of selecting for drug resistance, since it was with African trypanosomes that Paul Ehrlich performed such an experiment for the first time, more than a century ago. While breakthroughs in reverse genetics and genome editing have greatly facilitated the identification and validation of candidate resistance mutations in the trypanosomatids, the forward selection of drug-resistant mutants still relies on standard in vivo models and in vitro culture systems. Critical questions are: is selection for drug resistance performed in vivo or in vitro? With the mammalian or with the insect stages of the parasites? Under steady pressure or by sudden shock? Is a mutagen used? While there is no bona fide best approach, we think that a methodical consideration of these questions provides a helpful framework for selection of parasites for drug resistance in the laboratory.

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Iqbal, Mir Javid, Mohammad Ishaq Geer, and Parvez Ahamd Dar. "DEVELOPMENT OF AN INDICATOR BASED TOOL FOR THE ASSESSMENT OF MEDICINES SELECTION PRACTICES IN VARIOUS PUBLIC SECTOR HOSPITALS." International Journal of Pharmacy and Pharmaceutical Sciences 8, no. 11 (October 28, 2016): 205. http://dx.doi.org/10.22159/ijpps.2016v8i11.14536.

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Objective: To develop and validate an indicator based assessment tool for carrying out an in-depth assessment of drug selection practices prevalent at various public health facilities.Methods: The study was designed across the different levels of healthcare settings. One super specialty children’s tertiary care hospital (CH), one government medical college hospital (MCH), one district hospital (DH), one sub-district hospital (SDH) and one primary health center (PHC) was selected for the study. A set of 29 qualitative and 7 quantitative indicators was developed and validated to evaluate and assess drug selection practices in these public healthcare facilities.Results: All the surveyed healthcare facilities managed and stored essential medicines except solid oral dosage forms, contraceptives, antiretrovirals and anti-tubercular drugs. The selection process for medicines was not carried out at SDH and PHC level. National List of Essential Medicines (NLEM), manufacturer’s information, and expert faculty opinion were used as basic tools for carrying out a selection of drugs. None of the facilities were found to have Drugs and Therapeutics Committee (DTC) in place or its own Hospital Formulary (HF). Insufficient and irregular disbursement of funds was found to be the main constraint in selecting drugs for procurement. None of the facilities had any policy and procedural manual or standard operating procedures for governing their drug selection and quantification processes. Quantitative assessments showed that disbursement of the budget was fragmented. On the basis of NLEM children’s hospital was found to have highest medicines percentage availability of eighty percent (80%) with the lowest at SDH and PHC of twenty percent each (20%). Facility wise percentage adherence to various indicators for policies and procedures was found to be 50% in MCH, CH, and DH respectively and 66.6% adherence for NLEM indicator was recorded in MCH, CH and DH respectively.Conclusion: Drug selection process for public health facilities is a highly technical and professional activity that can only be achieved efficiently by having a well-defined document containing Standard Operating Procedures (SOPs) and comprehensive policy framework for drug selection, quantification, procurement, storage, distribution, and use besides having suitably qualified, adequately trained, sufficiently skilled manpower both at managerial and ground level.

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Belialov, F. I. "Selection of medications in comorbidity." Russian Archives of Internal Medicine 10, no. 1 (January 31, 2020): 57–60. http://dx.doi.org/10.20514/2226-6704-2020-10-1-57-60.

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New classification divides medications on five classes by influence on comorbid diseases and conditions and rates drug’s effects as favourable (A), possible (B), neutral (C), undesirable (D), and unfavourable (X). Class A includes drugs used in treatment of comorbid disease, class B embraced drugs with positive influence, class C includes drugs without significant influence or contradictory influence, class D consist of drugs with possible nonsevere adverse effects, and class X includes drugs with severe adverse effects. The more universal drug classification according to influence on comorbid diseases can include and unite other classifications. Classification may help unify marks of positive and negative influences drugs on comorbidity and help practitioners in selection of effective and safe treatment.

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Mott, David A., Jon C. Schommer, William R. Doucette, and David H. Kreling. "Agency Theory, Drug Formularies, and Drug Product Selection: Implications for Public Policy." Journal of Public Policy & Marketing 17, no. 2 (September 1998): 287–95. http://dx.doi.org/10.1177/074391569801700211.

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The authors describe the pharmaceutical utilization system and present the conceptual framework for agency theory. They then apply agency theory to the selection of pharmaceuticals and the role of drug formularies in drug selection. The use of drug formularies can increase the goal conflict and uncertainty related to the selection of drug products. The authors address public policy and research directions to suggest ways of reducing the level of goal conflict and uncertainty associated with drug selection. Recognition of agency relationships and the environment surrounding agency relationships appear to be important for the development and analysis of future policy regarding selection decisions pertaining to pharmaceuticals.

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&NA;. "Appropriate drug selection minimises performance impairment." Reactions Weekly &NA;, no. 427 (November 1992): 3. http://dx.doi.org/10.2165/00128415-199204270-00005.

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Shankar, Ravi P. "P-drug selection: Choosing medicines objectively." Nepal Journal of Epidemiology 3, no. 2 (June 26, 2013): 251. http://dx.doi.org/10.3126/nje.v3i2.8510.

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I read with great interest the article by Banerjee I. about ‘Concepts of P drug selection’ in the current issue of the Nepal Journal of Epidemiology. Personal (P) drug selection is an excellent exercise to teach students to prescribe medicines based on objective criteria and unbiased sources of medicine information. I had been involved in initiating the exercise on P drug selection at Manipal College of Medical Sciences, Pokhara. I had also been involved with the exercise at KIST Medical College, Lalitpur. Recently I have started this exercise at the Xavier University School of Medicine at Aruba, Dutch Caribbean.DOI: http://doi.dx.org/10.3126/nje.v3i2.8510

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Flammer, Keven. "Antibiotic Drug Selection in Companion Birds." Journal of Exotic Pet Medicine 15, no. 3 (July 2006): 166–76. http://dx.doi.org/10.1053/j.jepm.2006.06.003.

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Appelbaum, Peter C. "Resistance amongStreptococcus pneumoniae:Implications for Drug Selection." Clinical Infectious Diseases 34, no. 12 (June 15, 2002): 1613–20. http://dx.doi.org/10.1086/340400.

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PODRID, P. "Systematic approach to antiarrhythmic drug selection." Journal of Molecular and Cellular Cardiology 18 (1986): 57. http://dx.doi.org/10.1016/s0022-2828(86)80200-0.

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Li, Gang, Yining Wang, and S. Peter Ouyang. "Interim Treatment Selection in Drug Development." Statistics in Biosciences 1, no. 2 (November 2009): 268–88. http://dx.doi.org/10.1007/s12561-009-9017-y.

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Kaminski, James J. "Computer-assisted drug design and selection." Advanced Drug Delivery Reviews 14, no. 2-3 (June 1994): 331–37. http://dx.doi.org/10.1016/0169-409x(94)90049-3.

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Yang, Yanling. "Drug Selection Preference in Hypertensive Patients." Metabolism 104 (March 2020): 154094. http://dx.doi.org/10.1016/j.metabol.2019.12.040.

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He, Jiang, and Paul K. Whelton. "Selection of initial antihypertensive drug therapy." Lancet 356, no. 9246 (December 2000): 1942–43. http://dx.doi.org/10.1016/s0140-6736(00)03300-6.

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Broughton, Howard B., and Ian A. Watson. "Selection of heterocycles for drug design." Journal of Molecular Graphics and Modelling 23, no. 1 (September 2004): 51–58. http://dx.doi.org/10.1016/j.jmgm.2004.03.016.

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Armstrong, Edward P., Alexander Bykov, and Anthony V. Savelli. "Introducing formulary drug selection in Russia." American Journal of Health-System Pharmacy 53, no. 4 (February 15, 1996): 426–28. http://dx.doi.org/10.1093/ajhp/53.4.426.

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Barends, Clemens R. M., Anthony R. Absalom, and Michel M. R. F. Struys. "Drug selection for ambulatory procedural sedation." Current Opinion in Anaesthesiology 31, no. 6 (December 2018): 673–78. http://dx.doi.org/10.1097/aco.0000000000000652.

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BHATHENA, A., and B. SPEAR. "Pharmacogenetics: improving drug and dose selection." Current Opinion in Pharmacology 8, no. 5 (October 2008): 639–46. http://dx.doi.org/10.1016/j.coph.2008.07.013.

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Muegge, Ingo. "Selection criteria for drug-like compounds." Medicinal Research Reviews 23, no. 3 (March 14, 2003): 302–21. http://dx.doi.org/10.1002/med.10041.

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G., Priyadarshini Bai, and Ravi Kumar P. "A study on concept of P- drug selection among rural general practitioners." International Journal of Basic & Clinical Pharmacology 6, no. 1 (December 24, 2016): 155. http://dx.doi.org/10.18203/2319-2003.ijbcp20164772.

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Background: The objective of the study was to assess the awareness of P- drug selection among rural general practitioners’s (GP) for common medical conditions.Methods: Fifty general practitioners in Tumakuru district were provided with proformas for selection of P- drugs for mild to moderate hypertension, diabetes, upper respiratory tract infections and acid peptic disease based on safety, affordability, need, and efficacy (SANE criteria).Results: Forty one GP’s responded by completing the proformas. Seventeen of them were aware of the concept of P- drug selection. In hypertension, beta blockers followed by Angiotensin Converting Enzyme (ACE) inhibitors were most commonly preferred. In diabetes, biguanides followed by sulfonylureas were preferred as oral hypoglycemic agents. Ampicillin, Ciprofloxacin and Cotrimoxazole were the commonly used antibiotics for upper respiratory tract infections. Ranitidine and antacids were preferred for acid peptic disease. Affordability followed by efficacy was the deciding criteria for P- drug selection.Conclusions: There is lack of awareness of P- drug selection among many rural GP’s. Therefore, there is necessity to create awareness about P- drug selection through continued medical education for rational use of drugs.

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Tillie, Nicole A., Jayson L. Parker, and Jordan J. Feld. "Clinical Trial Risk in Hepatitis C: Endpoint Selection and Drug Action." Canadian Journal of Gastroenterology and Hepatology 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/6260271.

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Background and Aims.This study analyzed the risk of clinical trial failure of new drugs for hepatitis C between January 1998 and January 2015.Methods.Hepatitis C drug development trials that were in phases I–III of clinical trial testing were obtained from the publicly accessible clinical trial repository and other publicly available databases. Drug compounds were excluded from the study if they began their phase I testing before 1998, if they were not industry sponsored, or if they treated secondary complications of hepatitis C. Clinical trial success rates were analyzed in comparison to industry expectations. Further analysis was conducted on the molecule classifications, the mechanisms of action, and the trial endpoints.Results.One hundred and twenty-three unique drug compounds were found to fulfill the inclusion criteria, eight of which had FDA approval. The overall cumulative pass rate for hepatitis C drugs was 20%, which is double the industry expectation rate. Viral inhibitor small molecule drugs significantly reduced the risk of drug failure during clinical trials compared to other mechanisms of action.Conclusion.On average, one in every five drugs that began clinical testing will be approved for market. Viral inhibitor small molecule drugs are the most promising and hold the least risk.

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Fanita, Dina, and Bosker Sinaga. "Supplier Selection Decision Support System Drug Wighted Methods Product (WP)." Journal Of Computer Networks, Architecture and High Performance Computing 2, no. 1 (January 1, 2020): 135–39. http://dx.doi.org/10.47709/cnapc.v2i1.377.

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Supplier is very important for companies in services or goods, as well as parts procurement of medicines a Hospital, Hospital GrandMed is one of the largest hospital in Deli Serdang it's important to select suppliers for the procurement of drugs to be maintained and sustained quality of drug availabilitydrug and drug procurement logistics section to selectively choose a supplier with specific criteria so that the process of drug procurement at the Hospital GrandMed running properly and efficiently. The purpose of this study is to develop a decision support system to determine which is the best supplier priority Hospital medicine order so that can always keep the availability of drugs in terms of both the quality of medicines, price and delivery time. Data supplier in view of the historical kesupplier reservations about the company address, price, variants possessed drugs and drug quality and backed by the method of Weighted Product to support the decision which supplier to choose a priority determination. The results of this research program is to create a ranking and determination in the supplier selection GrandMed Hospital.Supplier selection decision support system with Weigthed method GranMed Product Hospital has been able to be built with UML and created with Visual Basic programming language in 2008.

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Tuti, Sri Dias, Umi Athiyah, and Wahyu Utami. "Factors Affecting The Drugs Availability on Program Rujuk Balik (PRB) at PRB Drugs Facility in Ex Karesidenan Kediri (Study of Hipertension Drugs)." JURNAL ILMU KEFARMASIAN INDONESIA 16, no. 1 (April 27, 2018): 30. http://dx.doi.org/10.35814/jifi.v16i1.437.

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PRB drugs service was given to chronically patients who still need long term treatment therefore there was need to guarantee the availability of medicines for their therapeutic needs. The objectives of this research were to know about PRB drug management, to know drug management support, policy, the availability of PRB drugs, and to analize the effects of drug management, drug management support, and policy on drug availability in PRB drug facility in Ex Karesidenan Kediri. Study on the availability of drugs hypertension due to prevalence of hypertension disease tends to increase from 7.6% in 2007 to 9.5% in 2013. This research used cross sectional approach by giving questionnaires to 18 respondents of PRB drug managers who had fulfilled the inclusion criteria. Those respondents were measured on the capability in PRB drug managements and assessment of PRB drug management support and policy. The questionnaires were valid and reliable. The management, management support, and policy score were tested for effect on PRB drug availability for Hypertension by using multiple linear regression. From this research, it was known that PRB Drug Management, PRB Drug Management Supporting Assessment, and PRB Policy by PRB drug managers in drug service facilities in Ex Karesidenan Kediri were mostly categorized as good enough. The availability of PRB drugs at PRB drugs facility with stock calculations indicated the ability to provide PRB medication to serve PRB prescription of hypertension was 28.71%. While the facilitieas that were able to serve an average of 72.67, thus providing substitution drugs beyond the average of PRB stock of 43.95%. The efforts were done for examples, borrowed similar drugs from regular supplies, medicines administered when they were available, or established the similar drugs at prices closed to e-catalogue. The results of the analysis showed that simultaneously PRB Drug Management factors, PRB Drug Management Supports, and PRB Policy have significant effects on PRB Drug Availability (p<0.10). PRB drug management had the greatest effects on the availability (p<0.10), especially in the case of Drug Selection (p<0.10). Selection of drugs was the first step to be done from a cycle of drug management, where the early step was the step that most determine the next steps. The pharmaceutical management framework affected the availability of PRB drugs, particularly the Drug Management factor, especially in the drug selection step. Drug selection was an early step of the drug management cycle that determined the next step.

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VandeWaa, Elizabeth A., and Carolyn Dolan. "Mindful Prescribing: Drug Development, Drug Selection, and Advanced Nursing Practice." Journal for Nurse Practitioners 16, no. 3 (March 2020): 186–90. http://dx.doi.org/10.1016/j.nurpra.2019.12.014.

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Böger, R. H. "Drug interactions of the statins and consequences for drug selection." Int. Journal of Clinical Pharmacology and Therapeutics 39, no. 09 (September 1, 2001): 369–82. http://dx.doi.org/10.5414/cpp39369.

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Mrazek, David A. "The Context of Genetic Testing in Clinical Psychiatric Practice." CNS Spectrums 11, S3 (March 2006): 3–4. http://dx.doi.org/10.1017/s1092852900025578.

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AbstractAlthough most patients with depression ultimately respond to antidepressant therapy, >50% have inadequate response to an individual antidepressant trial. The desire to avoid adverse drug reactions is common among patients, and is an important determinant of drug selection among psychiatrists. However, since the major classes of antidepressants and antipsychotics appear to be comparable in efficacy, clinicians have little basis for selecting the most effective agent for an individual patient. Pharmacogenetics, often described as the study of genetic variation that explains differential response to medication, represents an important new avenue toward improving treatment outcomes. Genetic variation in drug-metabolizing enzymes has been recognized for decades. The main focus of current psychiatric pharmacogenetic testing is on the cytochrome P450 (CYP) 2D6 and, to a somewhat lesser extent, on the 2C19 genes. Data suggest that poor metabolizer status can be associated with an increased risk of adverse drug reactions with certain medications, and that ultra-rapid metabolizers may require higher-than-usual doses to achieve a therapeutic response. The importance of CYP enzymes in the metabolism of several antidepressant and antipsychotic drugs suggest that genetic variation may aid in medication selection or dosing. Advances in pharmacogenetic research may facilitate the development of personalized medicine in which genetic information can inform drug selection, leading to optimal drug effectiveness and minimal drug toxicity.

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Bottery, Michael J., A. Jamie Wood, and Michael A. Brockhurst. "Selective Conditions for a Multidrug Resistance Plasmid Depend on the Sociality of Antibiotic Resistance." Antimicrobial Agents and Chemotherapy 60, no. 4 (January 19, 2016): 2524–27. http://dx.doi.org/10.1128/aac.02441-15.

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ABSTRACTMultidrug resistance (MDR) plasmids frequently carry antibiotic resistance genes conferring qualitatively different mechanisms of resistance. We show here that the antibiotic concentrations selecting for the RK2 plasmid inEscherichia colidepend upon the sociality of the drug resistance: the selection for selfish drug resistance (efflux pump) occurred at very low drug concentrations, just 1.3% of the MIC of the plasmid-free antibiotic-sensitive strain, whereas selection for cooperative drug resistance (modifying enzyme) occurred at drug concentrations exceeding the MIC of the plasmid-free strain.

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MacRaild, Christopher A., Muzaffar-Ur-Rehman Mohammed, Faheem, Sankaranarayanan Murugesan, Ian K. Styles, Amanda L. Peterson, Carl M. J. Kirkpatrick, et al. "Systematic Down-Selection of Repurposed Drug Candidates for COVID-19." International Journal of Molecular Sciences 23, no. 19 (October 6, 2022): 11851. http://dx.doi.org/10.3390/ijms231911851.

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SARS-CoV-2 is the cause of the COVID-19 pandemic which has claimed more than 6.5 million lives worldwide, devastating the economy and overwhelming healthcare systems globally. The development of new drug molecules and vaccines has played a critical role in managing the pandemic; however, new variants of concern still pose a significant threat as the current vaccines cannot prevent all infections. This situation calls for the collaboration of biomedical scientists and healthcare workers across the world. Repurposing approved drugs is an effective way of fast-tracking new treatments for recently emerged diseases. To this end, we have assembled and curated a database consisting of 7817 compounds from the Compounds Australia Open Drug collection. We developed a set of eight filters based on indicators of efficacy and safety that were applied sequentially to down-select drugs that showed promise for drug repurposing efforts against SARS-CoV-2. Considerable effort was made to evaluate approximately 14,000 assay data points for SARS-CoV-2 FDA/TGA-approved drugs and provide an average activity score for 3539 compounds. The filtering process identified 12 FDA-approved molecules with established safety profiles that have plausible mechanisms for treating COVID-19 disease. The methodology developed in our study provides a template for prioritising drug candidates that can be repurposed for the safe, efficacious, and cost-effective treatment of COVID-19, long COVID, or any other future disease. We present our database in an easy-to-use interactive interface (CoviRx that was also developed to enable the scientific community to access to the data of over 7000 potential drugs and to implement alternative prioritisation and down-selection strategies.

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Park, Andrew W., James Haven, Ray Kaplan, and Sylvain Gandon. "Refugia and the evolutionary epidemiology of drug resistance." Biology Letters 11, no. 11 (November 2015): 20150783. http://dx.doi.org/10.1098/rsbl.2015.0783.

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Drug resistance is a long-standing economic, veterinary and human health concern in human and animal populations. Efficacy of prophylactic drug treatments targeting a particular pathogen is often short-lived, as drug-resistant pathogens evolve and reach high frequency in a treated population. Methods to combat drug resistance are usually costly, including use of multiple drugs that are applied jointly or sequentially, or development of novel classes of drugs. Alternatively, there is growing interest in exploiting untreated host populations, refugia , for the management of drug resistance. Refugia do not experience selection for resistance, and serve as a reservoir for native, drug-susceptible pathogens. The force of infection from refugia may dilute the frequency of resistant pathogens in the treated population, potentially at an acceptable cost in terms of overall disease burden. We examine this concept using a simple mathematical model that captures the core mechanisms of transmission and selection common to many host–pathogen systems. We identify the roles of selection and gene flow in determining the utility of refugia.

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Bruni, J. "Antiepileptic Drug Selection and Adverse Effects: An Overview." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 21, S3 (August 1994): S3—S6. http://dx.doi.org/10.1017/s0317167100040749.

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Abstract:In choosing an antiepileptic drug, not only efficacy but also potential adverse effects have to be considered. Adverse effects that have to be taken into account include acute and chronic systemic toxicity, cognitive side effects, and teratogenesis. Acute toxicity may be dose-related, allergic or an idiosyncratic reaction. Chronic toxicity may involve the nervous system or other organs. In determining the role of new antiepileptic drugs such as lamotrigine, vigabatrin, felbamate, and gabapentin a proper evaluation of both efficacy and adverse effects is required.

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Perbawawati, Anna Adi, Endang Sugiharti, and Much Aziz Muslim. "Bayes Theorem and Forward Chaining Method On Expert System for Determine Hypercholesterolemia Drugs." Scientific Journal of Informatics 6, no. 1 (May 24, 2019): 116–24. http://dx.doi.org/10.15294/sji.v6i1.14149.

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The development of technology capable to imitating the process of human thinking and led to a new branch of computer science named the expert system. One of the problem that can be solved by an expert system is selecting hypercholesterolemia drugs. Drug selection starts from find the symptoms and then determine the best drug for the patient. This is consist with the mechanism of forward chaining which starts from searching for information about the symptoms, and then try to illustrate the conclusions. To accommodate the missing fact, expert systems can be complemented with the Bayes theorem that provides a simple rule for calculating the conditional probability so the accuracy of the method approaches the accuracy of the experts. This reseacrh uses 30 training data and 76 testing data of medical record that use hypercholesterolemia drugs from Tugurejo Hospital of Semarang. The variable are common symptoms and some hypercholesterolemia drugs. This research obtained a selection of hypercholesterolemia drugs system with 96.05% accuracy

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Pilon, Dominic, Lorie A. Ellis, Yongling Xiao, Ajay S. Behl, and Patrick Lefebvre. "Consideration of Potential Drug–Drug Interactions in Selection of FDA-Approved Drugs Indicated for Prostate Cancer." American Journal of Therapeutics 26, no. 3 (2019): e422-e424. http://dx.doi.org/10.1097/mjt.0000000000000693.

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Rome, Benjamin N., Sarosh Nagar, Alexander C. Egilman, Junyi Wang, William B. Feldman, and Aaron S. Kesselheim. "Simulated Medicare Drug Price Negotiation Under the Inflation Reduction Act of 2022." JAMA Health Forum 4, no. 1 (January 27, 2023): e225218. http://dx.doi.org/10.1001/jamahealthforum.2022.5218.

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ImportanceThe Inflation Reduction Act of 2022 gives Medicare the authority to negotiate prices for certain prescription drugs. Which drugs will be selected and how prices will be negotiated remain unclear.ObjectiveTo simulate drug selection and the minimum savings that would have been achieved at statutory ceiling prices if Medicare drug price negotiation had been implemented from 2018 to 2020.Design, Setting, and ParticipantsIn this cross-sectional study, a policy simulation analysis of high-spending prescription drugs in Medicare Part B and Part D that were eligible for negotiation from January 2018 to December 2020 was performed from August 5 to November 20, 2022.ExposuresEligibility criteria for selection and discounts afforded by the statutory ceiling prices for negotiation.Main Outcomes and MeasuresThe main outcomes were characteristics of drugs subject to negotiation and estimated Medicare savings from 2018 to 2020 that would have been achieved through spending at ceiling prices compared with existing net prices accounting for price concessions.ResultsAmong the 40 selected drugs, 35 were primarily reimbursed through Medicare Part D and 5 through Part B and 10 were biologics. The most common therapeutic classes were endocrine (11), neurologic or psychiatric (5), pulmonary (4), rheumatologic or immunologic (4), and cardiovascular (4). Median time from US Food and Drug Administration approval to selection was 12 years (IQR, 10-14 years). Three drugs faced generic competition in the 2 years between selection and price negotiation. For the remaining 37 drugs, estimated net Medicare spending from 2018 to 2020 was $55.3 billion; spending at ceiling prices would have been reduced by an estimated $26.5 billion, which represented 5% of estimated net Medicare drug spending during those 3 years.Conclusions and RelevanceIn this cross-sectional study, simulating the drug price negotiation provisions in the Inflation Reduction Act of 2022 revealed important limitations, including strict selection criteria and the potential for drugs to become ineligible for negotiation during the 2 years between selection and prices taking effect. Despite these limitations, the policy still delivered substantial savings because ceiling prices offered steep discounts, in part, by erasing excess spending from price increases faster than inflation.

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Tiahunova, M. Yu, N. O. Ilina, and T. V. Holub. "INTELLIGENT DECISION SUPPORT SYSTEM FOR DRUG SELECTION." Problems of Modeling and Design Automatization 21, no. 1 (2019): 16–22. http://dx.doi.org/10.31474/2074-7888-2019-1-16-22.

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Shankar, Ravi. "Seven years’ experience of P-drug selection." Australasian Medical Journal 4, no. 4 (May 1, 2011): 201–4. http://dx.doi.org/10.4066/amj.2011.648.

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Journal articles on the topic 'Drug selection'

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