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Готові списки джерел за темами / Pharmacokinetic interactions

Добірка наукової літератури з теми "Pharmacokinetic interactions"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 21 лютого 2023

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Статті в журналах з теми "Pharmacokinetic interactions":

1

Taylor, David. "Pharmacokinetic interactions involving clozapine." British Journal of Psychiatry 171, no. 2 (August 1997): 109–12. http://dx.doi.org/10.1192/bjp.171.2.109.

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BackgroundMetabolism of clozapine is complex and not fully understood. Pharmacokinetic interactions with other drugs have been described but, in some cases, their mechanism is unknown.MethodPublished trials and case reports relevant to the human metabolism of clozapine and to suspected pharmacokinetic interactions were reviewed.ResultsMetabolism of clozapine appears to be largely controlled by the function of the hepatic cytochrome p4501A2 (CYPIA2). Compounds which induce CYPIA2 activity (carbamazepine, tobacco smoke) may reduce plasma clozapine levels. Inhibitors of CYPIA2 (caffeine, erythromycin) have the opposite effect. Drugs which inhibit the hepatic cytochrome p4502D6 (CYP2D6) have also been reported to elevate plasma clozapine levels. The mechanism of this interaction is unclear.ConclusionsThe co-administration of clozapine and compounds reported to alter its metabolism should be avoided where possible. A host of other interactions can be predicted and so caution should be exercised when co-administering drugs which affect the function of CYPIA2 and CYP2D6. The pharmacokinetics of clozapine require further investigation so that its safe use can be assured.

2

Keirns, J., T. Sawamoto, M. Holum, D. Buell, W. Wisemandle, and A. Alak. "Steady-State Pharmacokinetics of Micafungin and Voriconazole after Separate and Concomitant Dosing in Healthy Adults." Antimicrobial Agents and Chemotherapy 51, no. 2 (November 20, 2006): 787–90. http://dx.doi.org/10.1128/aac.00673-06.

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ABSTRACT We assessed the pharmacokinetics and interactions of steady-state micafungin (Mycamine) or placebo with steady-state voriconazole in 35 volunteers. The 90% confidence intervals around the least-squares mean ratios for micafungin pharmacokinetic parameters and placebo-corrected voriconazole pharmacokinetic parameters were within the 80%-to-125% limits, indicating an absence of drug interaction.

3

Soyata, Amelia, Aliya Nur Hasanah, and Taofik Rusdiana. "Interaction of Warfarin with Herbs Based on Pharmacokinetic and Pharmacodynamic Parameters." Indonesian Journal of Pharmaceutics 2, no. 2 (June 5, 2020): 69. http://dx.doi.org/10.24198/idjp.v2i2.27289.

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Warfarin is an oral anticoagulant that has been widely used and has strong efficacy, but the use of warfarin is still a concern because of its narrow therapeutic index which cause interactions when co-administration with drugs, herbs or food. This interaction can affect the pharmacokinetics and pharmacodynamics of warfarin and the most fatal effect from warfarin interactions is bleeding. In this review article data on warfarin-herbs interactions were collected based on pharmacokinetic parameters (AUC0-∞, Cmax, T1/2, Cl/F, and V/F), while pharmacodynamic parameters (International normalized ratio (INR), platelet aggregation, AUC INR and Protombine Time). As a result some herbs had significant interactions with warfarin. Herbs that affect warfarin pharmacokinetic were Danshen gegen, echinacea, St. John's wort and caffeine and herbs that affect pharmacodynamic were policosanol, Ginkgo biloba, cranberry, St. John's wort, ginseng, pomegranate, Psidium guajava and curcumin, so co-administration warfarin with herbs need to be considered.Keywords: Warfarin, Interactions, Herbs, Pharmacokinetics, Pharmacodynamics

4

Costache, Irina-Iuliana, Anca Miron, Monica Hăncianu, Viviana Aursulesei, Alexandru Dan Costache, and Ana Clara Aprotosoaie. "Pharmacokinetic Interactions between Cardiovascular Medicines and Plant Products." Cardiovascular Therapeutics 2019 (September 2, 2019): 1–19. http://dx.doi.org/10.1155/2019/9402781.

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The growing use of plant products among patients with cardiovascular pharmacotherapy raises the concerns about their potential interactions with conventional cardiovascular medicines. Plant products can influence pharmacokinetics or/and pharmacological activity of coadministered drugs and some of these interactions may lead to unexpected clinical outcomes. Numerous studies and case reports showed various pharmacokinetic interactions that are characterized by a high degree of unpredictability. This review highlights the pharmacokinetic clinically relevant interactions between major conventional cardiovascular medicines and plant products with an emphasis on their putative mechanisms, drawbacks of herbal products use, and the perspectives for further well-designed studies.

5

ERESHEFSKY, LARRY, STEPHEN R. SAKLAD, MARK D. WATANABE, CHESTER M. DAVIS, and MICHAEL W. JANN. "Thiothixene Pharmacokinetic Interactions." Journal of Clinical Psychopharmacology 11, no. 5 (October 1991): 296???301. http://dx.doi.org/10.1097/00004714-199110000-00004.

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6

Hartshorn, Edward A. "Pharmacokinetic Drug Interactions." Journal of Pharmacy Technology 1, no. 5 (September 1985): 193–99. http://dx.doi.org/10.1177/875512258500100505.

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7

Eichelbaum, Michel. "Pharmacokinetic Drug Interactions." Journal of Clinical Pharmacology 26, no. 6 (July 8, 1986): 469–73. http://dx.doi.org/10.1002/j.1552-4604.1986.tb03560.x.

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8

Pukrittayakamee, Sasithon, Joel Tarning, Podjanee Jittamala, Prakaykaew Charunwatthana, Saranath Lawpoolsri, Sue J. Lee, Warunee Hanpithakpong, et al. "Pharmacokinetic Interactions between Primaquine and Chloroquine." Antimicrobial Agents and Chemotherapy 58, no. 6 (March 31, 2014): 3354–59. http://dx.doi.org/10.1128/aac.02794-13.

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ABSTRACTChloroquine combined with primaquine has been the standard radical curative regimen forPlasmodium vivaxandPlasmodium ovalemalaria for over half a century. In an open-label crossover pharmacokinetic study, 16 healthy volunteers (4 males and 12 females) aged 20 to 47 years were randomized into two groups of three sequential hospital admissions to receive a single oral dose of 30 mg (base) primaquine, 600 mg (base) chloroquine, and the two drugs together. The coadministration of the two drugs did not affect chloroquine or desethylchloroquine pharmacokinetics but increased plasma primaquine concentrations significantly (P≤ 0.005); the geometric mean (90% confidence interval [CI]) increases were 63% (47 to 81%) in maximum concentration and 24% (13 to 35%) in total exposure. There were also corresponding increases in plasma carboxyprimaquine concentrations (P≤ 0.020). There were no significant electrocardiographic changes following primaquine administration, but there was slight corrected QT (QTc) (Fridericia) interval lengthening following chloroquine administration (median [range] = 6.32 [−1.45 to 12.3] ms;P< 0.001), which was not affected by the addition of primaquine (5.58 [1.74 to 11.4] ms;P= 0.642). This pharmacokinetic interaction may explain previous observations of synergy in preventingP. vivaxrelapse. This trial was registered at ClinicalTrials.gov under reference number NCT01218932.

9

Cohen, Lawrence J., and C. Lindsay DeVane. "Clinical Implications of Antidepressant Pharmacokinetics and Pharmacogenetics." Annals of Pharmacotherapy 30, no. 12 (December 1996): 1471–80. http://dx.doi.org/10.1177/106002809603001216.

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OBJECTIVE: To review available data on pharmacokinetic and pharmacogenetic influences on the response to antidepressant therapy, analyze the mechanisms for and clinical significance of pharmacokinetic and pharmacogenetic differences, and explain the implications of pharmacokinetics and pharmacogenetics for patient care. DATA SOURCES: A MEDLINE search of English-language clinical studies, abstracts, and review articles on antidepressant pharmacokinetics, pharmacogenetics, and drug interactions was used to identify pertinent literature. DATA SYNTHESIS: The pharmacokinetic profiles of selected antidepressants are reviewed and the impact of hepatic microsomal enzymes on antidepressant metabolism is considered. How phenotypic differences influence the metabolism of antidepressant drug therapy is addressed. To evaluate the clinical implications of these pharmacokinetic and pharmacogenetic considerations, the findings of studies designed to elucidate drug interactions involving antidepressant agents are discussed. CONCLUSIONS: Differences in antidepressant plasma concentrations, and possibly safety, are caused by polymorphism in the genes that encode some of the cytochrome P450 isoenzymes that metabolize antidepressants. The isoenzymes 1A2, 2C9/19, 2D6, and 3A4 are the major enzymes that catalyze antidepressant metabolic reactions. Antidepressants can be either substrates or inhibitors of these enzymes, which also metabolize many other pharmacologic agents. Although the cytochrome enzymes that metabolize antidepressants have not been fully characterized, interaction profiles of the newer antidepressants are becoming more clearly defined. Determining patient phenotypes is not practical in the clinical setting, but an awareness of the possibility of genetic polymorphism in antidepressant metabolism may help explain therapeutic failure or toxicity, help predict the likelihood of drug interactions, and help clinicians better manage antidepressant drug therapy.

10

Marvanova, Marketa. "Pharmacokinetic characteristics of antiepileptic drugs (AEDs)." Mental Health Clinician 6, no. 1 (January 1, 2016): 8–20. http://dx.doi.org/10.9740/mhc.2015.01.008.

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Abstract Antiepileptic drugs (AEDs) are routinely prescribed for the management of a variety of neurologic and psychiatric conditions, including epilepsy and epilepsy syndromes. Physiologic changes due to aging, pregnancy, nutritional status, drug interactions, and diseases (ie, those involving liver and kidney function) can affect pharmacokinetics of AEDs. This review discusses foundational pharmacokinetic characteristics of AEDs currently available in the United States, including clobazam but excluding the other benzodiazepines. Commonalities of pharmacokinetic properties of AEDs are discussed in detail. Important differences among AEDs and clinically relevant pharmacokinetic interactions in absorption, distribution, metabolism, and/or elimination associated with AEDs are highlighted. In general, newer AEDs have more predictable kinetics and lower risks for drug interactions. This is because many are minimally or not bound to serum proteins, are primarily renally cleared or metabolized by non–cytochrome P450 isoenzymes, and/or have lower potential to induce/inhibit various hepatic enzyme systems. A clear understanding of the pharmacokinetic properties of individual AEDs is essential in creating a safe and effective treatment plan for a patient.

Більше джерел

Дисертації з теми "Pharmacokinetic interactions":

1

McArdle, Elizabeth Karen. "Pharmacokinetic interactions of constituents of cannabis extracts." Thesis, University of Aberdeen, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.415480.

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The use of a whole plant cannabis extract, containing D⁹-tetrahydrocannabinol (THC) and cannabidiol (CBD) as the principal constituents, showed statistically significant improvements in the management of multiple sclerosis. Inhibition studies (e.g. IC₅₀ and K_i determinations) using phenotyped human liver microsomes and cDNA expressed human P450s (Supersomesâ demonstrated that CBD competitively inhibits the principal P450s involved in the THC biotransformation, CYP2C9 (K_i = 0.5 mM), CYP2C19) (K_i = 0.4 mM) and CYP3A4 (K_i = 0.07 mM. CBD inhibition of CYP3A4 was mechanism-based, which suggests that a CBD metabolite (e.g. CBD-hydroxyquinone) is involved in CYP3A4 inhibition. CBD differentially induced rat P450s, whereas THC had no discernible effects on rat P450s. CBD significantly increased CYP1A2 protein at 150 mg kg^-1, but showed no change in mRNA expression. In addition, CYP1A-dependent activity was inhibited by < 80 % by CBD. These results suggest that CBD may bind tightly to and modify the CYP1A2 active site, thereby stabilising the protein but preventing substrate interaction. The significant increase in CYP2B1 mRNA implies that CBD transcriptionally regulates CYP2B, perhaps by activating CAR or through “cross-talk” by PXR. The 4-fold increase in CYP3A23 mRNA level suggests that CBD may be a weak ligand for PXR or that CBD is acting via CAR, which can also bind to response elements on the CYP3A23 gene. CBD is a potent inhibitor of P450-catalysed THC metabolism in vitro however pharmacokinetic modelling predicted that the therapeutic level of CBD (low nM range) after sublingual co-administration of THC and CBD (10 mg of each) was insufficient to inhibit THC metabolism of other human volunteers. This does not rule out the potential for CBD to inhibit the metabolism of other co-administered drugs in vivo. CBD may also induce the human orthologues of rat P450s, mainly CYP2B6 and CYP3A4, following extended periods of administration at high doses.

2

Raaska, Kari. "Pharmacokinetic interactions of clozapine in hospitalized patients." Helsinki : University of Helsinki, 2003. http://ethesis.helsinki.fi/julkaisut/laa/kliin/vk/raaska/.

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3

Lundahl, Anna. "In vivo Pharmacokinetic Interactions of Finasteride and Identification of Novel Metabolites." Doctoral thesis, Uppsala universitet, Institutionen för farmaci, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-129362.

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The general aim of this thesis was to improve the understanding of the in vivo pharmacokinetics and, in particular, the metabolism of finasteride, a 5α-reductase inhibitor used in the treatment of enlarged prostate glands and male pattern baldness. CYP3A4 has been identified as the major enzyme involved in the sequential metabolism of finasteride to ω-OH finasteride (M1) and ω-COOH finasteride (M3). The consequences of induced and inhibited metabolism on the pharmacokinetics of finasteride and its metabolites were investigated in humans and pigs. Both studies included bile collection. The collected human and pig samples were used for the metabolite identification. As expected, induced metabolism led to reduced plasma exposure of finasteride and inhibited metabolism had the opposite effect. The interactions were investigated in detail and included examination of the biliary pharmacokinetics of finasteride and its metabolites. In pigs, the study included monitoring of the hepatic extraction over time, deconvolution and the development of a semi-physiological model for comparison of the effects on the gut wall and liver metabolism. For M3, the concentration ratios of bile to plasma and the renal clearance indicated that carrier-mediated processes are involved in the biliary and urinary excretion. This was not, however, the case for finasteride. The metabolite, M1, could not be quantified either in humans or pigs. Instead, two other OH metabolites, M1 isomers, were identified in humans. These metabolites were found to undergo glucuronide conjugation. In humans, one glucuronide was identified intact and in pigs, both glucuronides were identified intact in bile and in urine. In addition, a glucuronide of M3 was identified in human bile. In conclusion, advances have been made in the understanding of the pharmacokinetics of finasteride, in particular in relation to the metabolism. Hopefully, the findings of this comprehensive investigation can be applied to other drugs and novel chemical entities.

4

Adedoyin, A. P. "Pharmacokinetic drug-drug interactions : inhibition and induction studies in the rat." Thesis, University of Manchester, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.376236.

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5

Elsherbiny, Doaa. "Pharmacokinetic drug-drug interactions in the management of malaria, HIV and tuberculosis." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8426.

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6

Yadav, Jaydeep. "EVALUATING PHARMACOKINETIC DRUG-DRUG INTERACTIONS DUE TO TIME DEPENDENT INHIBITION OF CYTOCHROME P450s." Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/524248.

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Pharmaceutical Sciences
Ph.D.
Time-dependent inactivation (TDI) of CYPs is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to over-predict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human liver microsomes. Inhibitors evaluated include troleandomycin (TAO), erythromycin (ERY), verapamil (VER), Paroxetine (PAR), itraconazole (ITZ) and diltiazem (DTZ) along with primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (MA). Complexities incorporated in the models included multiple binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The different factors affecting TDI kinetics were evaluated such as lipid partitioning, inhibitor depletion, presence of transporters. The inactivation parameters obtained from numerical method were incorporated into static in-vitro – in-vivo correlation (IVIVC) models to predict clinical DDIs. For 123 clinically observed DDIs, using a hepatic CYP3A synthesis rate constant of 0.000146 min-1, the average fold difference between observed and predicted DDIs was 2.97 for the standard replot method and 1.66 for the numerical method. Similar results were obtained using a synthesis rate constant of 0.00032 min-1. These results suggest that numerical methods can successfully model complex in-vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach. Chapter one presents the detailed introduction along with the hypothesis and significance of the project. Chapter 2 includes the development of the bioanalytical method for quantitation of various compounds which includes inactivators and their primary metabolites. Chapter 3 entails the discussion on in-vivo studies in rats involving TDI mediated DDI studies. Chapter 4 discusses the in-vitro studies and use of the numerical method for evaluation of TDI kinetics. Chapter 5 and chapter 6 provides discussion on the impact of inhibitor depletion and partitioning of TDI kinetics and how these two could lead to misinterpretation of TDI results. Chapter 6 also provides a discussion on how transporters could affect TDI results mainly from hepatocyte studies. Chapter 7 involves prediction of TDI mediated DDI using static modeling. Chapter 8 is a case study on bosentan involving induction mediated DDI.
Temple University--Theses

7

Cherkaoui, Rbati Mohammed. "Mathematical and physical systems biology : application to pharmacokinetic drug-drug interactions and tumour growth." Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/33719/.

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In this thesis, a multi-scale approach is provided to a pharmacokinetic and a pharmacodynamic problem. The first part of this research provides a realistic mathematical physiological model of the liver to predict drug drug interactions (DDIs). The model describes the geometry of a lobule (liver unit) and integrates the exchange processes, diffusion and active transport, between the hepatocytes and the blood and possible drug-drug interactions such as; reversible inhibition, mechanistic based inhibition (MBI) and enzyme induction. The liver model is subsequently integrated into a PBPK model with 7 compartments (artery blood, venous blood, gut, liver, kidney, lung, rest of the body). To assess the efficiency of the model to predict DDIs, 77 clinical DDI studies were compared to the model. These 77 clinical studies represent 5 victim drugs (midazolam, simvastatin, triazolam, cerivastatin and nifedipine) and 30 perpetrator drugs. The reversible inhibition, MBI and induction parameters for the majority of the perpetrators were estimated with in vitro experiments and adjusted for the human liver size. The PK parameters, such as clearance and absorption rate, and the physiological parameters were obtained from the literature. The DDIs were measured as the ratio of the AUC (Area Under the Curve of the blood concentration) or the ratio of the maximum concentration Cmax of the victim drug administered with and without the perpetrator drug. The predicted ratios were compared with the clinical observation by calculating the geometric fold error GMFE. The GMFE for AUCratio and Cmax,ratio were calculated to be 1.54 and 1.34, respectively. Moreover, the PBPK model excluding the gut compartment under-predicts both inhibition (lower AUCratio) and induction (higher AUCratio) which strongly suggests that the gut DDI component can not be neglected for accurate clinical prediction. However, the static combined model by Fahmi et al. [1, 2] without the gut component fortuitously predicts the clinical AUCratio better than inclusion with the gut component. To conclude, the model predicts DDIs relatively well as it is in the lower range of errors reported in the literature (1.47-2.00 [1, 2]). Moreover, the model is able to predict the pharmacokinetics of drugs and provides a dynamic description of the DDIs, such as the enzyme level and spatial distribution within a lobule. Furthermore, the perpetrator dose regimen can be changed or the error in the in vitro parameters can be integrated to observe their influences on the AUC ratio. The second part of this research explored the Warburg effect in a avascular tumour growth model incorporating a cell shedding term to account for tumour shrinkage. The tumour model was based on an extension of the Ward and King model [3], where two sub-populations; living cells and dead cells are considered. Three diffusion equations for glucose, lactate and the drug are considered and included into the model for growth rate, natural death rate and a death rate due to the drug. The simulation of the model without a drug shows similar behaviour to the original model by Ward and King despite the presence of the shedding term and predicts an extracellular pH of 6.8. However, when a drug treatment is added, the model is able to simulate the shrinkage of the tumour unlike the original model. Moreover, two scenarios with a basic, neutral and acidic drug were explored, assuming similar efficiency at physiological pH to assess the effect of changes in the extracellular pH. Acidic or weak base drugs seem to be more efficient in low pH environment as the fraction of neutral form is greater and therefore more drug is available to cross the cell membrane to reach its target.

8

Naghmeh, Jabarizadekivi. "A Comparison of the Effect of Omeprazole and Rabeprazole on Clozapine Serum Concentrations." University of Sydney, 2008. http://hdl.handle.net/2123/2471.

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Master of Philosophy
Clozapine is a drug of choice for treatment of refractory schizophrenia, which is primarily metabolized by Cytochrome P450 1A2 (CYP1A2). Norclozapine is its main metabolite. There are reports of wide ranging gastrointestinal side effects associated with clozapine therapy, that result in concomitant administration of proton pump inhibitors to treat acid-related disorders. Omeprazole is an established CYP1A2 inducer, while an in vitro study has shown that rabeprazole is much less potent in this regard. There is no available information about the impact of rabeprazole on CYP1A2 activity in patients. Firstly, this information is essential when prescriptions are changed from omeprazole to rabeprazole to reduce medication costs. Therefore, the aim of this study was to compare the effects of rabeprazole and omeprazole on CYP1A2-mediated clearance (CL/F) of clozapine. Secondly, the effective dosage of clozapine varies widely among patients, making it necessary to individualize drug therapy with clozapine. The reason for dosage variation could be due to the influence of patient-related variables on clozapine plasma concentrations. Therefore, another aim of this study was to investigate the relationship between patient variables, such as age, gender, cigarette smoke, weight and body mass index and clozapine clearance (CL/F). A cross-over study design was used for this study. Twenty patients from Macquarie hospital who were receiving clozapine and rabeprazole (with no other interacting medications) were recruited in this study. Blood samples were taken at 30 min, 1 hr, 2 hr and 12 hr after a dose of clozapine. Rabeprazole was then replaced with omeprazole. After at least 1 month blood samples were again collected at the above corresponding intervals after clozapine. The plasma concentrations of clozapine and norclozapine were determined by high performance liquid chromatography. Abbottbase Pharmacokinetic Systems Software, which utilizes Bayesian forecasting, was used to estimate pharmacokinetic parameters of clozapine. The ratio of plasma norclozapine/clozapine concentrations at trough level was used to reflect CYP1A2 activity. No difference was observed in clozapine clearance (CL/F) and CYP1A2 activity during concurrent therapy with either rabeprazole or omeprazole. According to some studies CYP1A2 induction by omeprazole is dose dependent. Furthermore, since rabeprazole is a weak CYP1A2 inducer in vitro, we conclude that omeprazole and rabeprazole may not induce CYP1A2 activity when used at conventional therapeutic dosage (<40 mg/day). Hence, replacement of omeprazole with rabeprazole at conventional therapeutic dosages (20 or 40 mg daily) offers no advantages in the management of patients with schizophrenia on clozapine and no dose adjustment is required. Consistent with previous studies, clozapine concentrations were found to be significantly lower in cigarette smokers due to CYP1A2 induction. No relationship was found between age, gender, or weight and clozapine clearance (CL/F). However, body mass index showed a significant negative correlation with clozapine clearance (CL/F). Since weight gain and lipid accumulation are common side effects of clozapine they may be associated with a reduction of CYP1A2 activity and clozapine clearance (CL/F). Moreover, high lipoprotein levels may decrease the unbound fraction of clozapine and decrease the availability of clozapine for oxidation by cytochrome P450 enzymes. Therefore, it is concluded that omeprazole and rabeprazole may not induce CYP1A2 activity when used at conventional therapeutic dosage (<40mg/day). Hence, replacement of omeprazole with rabeprazole does not require the dose of clozapine to be adjusted. Moreover, the negative correlation between clozapine clearance (CL/F) and BMI is informative. Further studies are now required to clarify the relationship between BMI, lipoprotein levels and clozapine clearance in patients with schizophrenia.

9

Naghmeh, Jabarizadekivi. "A Comparison of the Effect of Omeprazole and Rabeprazole on Clozapine Serum Concentrations." Thesis, The University of Sydney, 2007. http://hdl.handle.net/2123/2471.

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Clozapine is a drug of choice for treatment of refractory schizophrenia, which is primarily metabolized by Cytochrome P450 1A2 (CYP1A2). Norclozapine is its main metabolite. There are reports of wide ranging gastrointestinal side effects associated with clozapine therapy, that result in concomitant administration of proton pump inhibitors to treat acid-related disorders. Omeprazole is an established CYP1A2 inducer, while an in vitro study has shown that rabeprazole is much less potent in this regard. There is no available information about the impact of rabeprazole on CYP1A2 activity in patients. Firstly, this information is essential when prescriptions are changed from omeprazole to rabeprazole to reduce medication costs. Therefore, the aim of this study was to compare the effects of rabeprazole and omeprazole on CYP1A2-mediated clearance (CL/F) of clozapine. Secondly, the effective dosage of clozapine varies widely among patients, making it necessary to individualize drug therapy with clozapine. The reason for dosage variation could be due to the influence of patient-related variables on clozapine plasma concentrations. Therefore, another aim of this study was to investigate the relationship between patient variables, such as age, gender, cigarette smoke, weight and body mass index and clozapine clearance (CL/F). A cross-over study design was used for this study. Twenty patients from Macquarie hospital who were receiving clozapine and rabeprazole (with no other interacting medications) were recruited in this study. Blood samples were taken at 30 min, 1 hr, 2 hr and 12 hr after a dose of clozapine. Rabeprazole was then replaced with omeprazole. After at least 1 month blood samples were again collected at the above corresponding intervals after clozapine. The plasma concentrations of clozapine and norclozapine were determined by high performance liquid chromatography. Abbottbase Pharmacokinetic Systems Software, which utilizes Bayesian forecasting, was used to estimate pharmacokinetic parameters of clozapine. The ratio of plasma norclozapine/clozapine concentrations at trough level was used to reflect CYP1A2 activity. No difference was observed in clozapine clearance (CL/F) and CYP1A2 activity during concurrent therapy with either rabeprazole or omeprazole. According to some studies CYP1A2 induction by omeprazole is dose dependent. Furthermore, since rabeprazole is a weak CYP1A2 inducer in vitro, we conclude that omeprazole and rabeprazole may not induce CYP1A2 activity when used at conventional therapeutic dosage (<40 mg/day). Hence, replacement of omeprazole with rabeprazole at conventional therapeutic dosages (20 or 40 mg daily) offers no advantages in the management of patients with schizophrenia on clozapine and no dose adjustment is required. Consistent with previous studies, clozapine concentrations were found to be significantly lower in cigarette smokers due to CYP1A2 induction. No relationship was found between age, gender, or weight and clozapine clearance (CL/F). However, body mass index showed a significant negative correlation with clozapine clearance (CL/F). Since weight gain and lipid accumulation are common side effects of clozapine they may be associated with a reduction of CYP1A2 activity and clozapine clearance (CL/F). Moreover, high lipoprotein levels may decrease the unbound fraction of clozapine and decrease the availability of clozapine for oxidation by cytochrome P450 enzymes. Therefore, it is concluded that omeprazole and rabeprazole may not induce CYP1A2 activity when used at conventional therapeutic dosage (<40mg/day). Hence, replacement of omeprazole with rabeprazole does not require the dose of clozapine to be adjusted. Moreover, the negative correlation between clozapine clearance (CL/F) and BMI is informative. Further studies are now required to clarify the relationship between BMI, lipoprotein levels and clozapine clearance in patients with schizophrenia.

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Salem, Farzaneh. "Applications of physiologically based pharmacokinetic modelling to prediction of the likelihood of metabolic drug interactions in paediatric population and studying disparities in pharmacokinetics between children and adults." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/applications-of-physiologically-based-pharmacokinetic-modelling-to-prediction-of-the-likelihood-of-metabolic-drug-interactions-in-paediatric-population-and-studying-disparities-in-pharmacokinetics-between-children-and-adults(1fdefe9a-037a-4738-b92a-5904a60960db).html.

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Anticipation of drug-drug interactions (DDIs) in the paediatric population are merely based on data generated in adults. Hence decision on avoiding certain combinations or attempts to adjust and manage the doses under combination-therapy are mainly speculative from the knowledge of what occurs in adults. However, due to developmental changes in elimination pathways from birth to adolescents, the assumption of DDIs being similar in adults and children might not be correct. This thesis firstly identifies and quantitatively compares the reported DDIs in paediatric and adult populations through a systematic literature review of DDIs reported in paediatric subjects. The study highlights the clear paucity of the data in children younger than 2 years. Therefore, the logical approach to test this hypothesis has been through modelling and simulation and incorporation of the biological knowledge on ontogeny of various enzymes and other elimination routes. The magnitude of any metabolic DDI depends on fractional importance of inhibited pathway which may not necessarily be the same in young children when compared to adults. To show this disparity between rate of ontogeny for metabolic pathways, the ontogeny pattern of CYP enzymes and renal function were analysed systematically. Bootstrap methodology was used to account for variability, and to define the age range over which a statistical difference is likely between each pair of specific pathways. A number of DDIs were simulated for virtual compounds to highlight the possibility that the magnitude of DDI can be influenced by age. Depending on the extent of contribution of metabolic pathways, neonates could be more sensitive to DDI than adults in certain scenarios or vice versa. Thus, extrapolation from adult DDI data may not be applicable across paediatric age groups. The uncertainty around the ontogeny functions based on in vitro information led us to carry out comprehensive performance verification for in vivo data on probe substrates of CYP1A2, -2C9 and 3A4 and assess the predictions of clearance (CL) by monitoring AUC. Although the evaluation showed that in most cases predictions were within two fold of observed data in adult and paediatric studies, the outcome suggests that the current ontogeny profiles result in under-prediction of CL values compared to clinical studies in infants and children and there is a need for better ontogeny models. Therefore, we derived novel ontogeny functions for CYP1A2 and CYP3A based on caffeine-theophylline and midazolam in vivo data. Age related CL data for caffeine, theophylline and midazolam were reconstructed back to intrinsic CL values per milligram of microsomal protein and best fit ontogeny models for CYP1A2 and CYP3A were derived from these data. The function for CYP1A2 describes an increase in relative intrinsic metabolic CL from birth to 3 years followed by a decrease to adult values. The function for CYP3A4 describes a continuous rise in relative intrinsic metabolic CL, reaching the adult value at about 2 years of age. The new models were validated by showing improved predictions of the systemic CL of ropivacaine (major CYP1A2 substrate; minor CYP3A4 substrate) and alfentanil (major CYP3A4 substrate) compared to those using a previous ontogeny function based on in vitro data. When implementing enzyme ontogeny functions it is important to consider potential confounding factors related to disease, anaesthesia and surgery that may affect the prediction of net in vivo CL. Finally, we demonstrated the application of paediatric physiologically-based pharmacokinetic (p-PBPK) models for calculation of sample size in paediatric clinical pharmacokinetic (PK) studies in a methodology suggested by Wang et al., based on desired precision for a PK parameter of interest. We obtained estimates of variability for CL, volume of distribution and area under the plasma concentration-time curve for 5 different drugs from (i) adult and paediatric classic clinical PK studies, and (ii) p-PBPK combined with in vitro-in vivo extrapolation. The estimates were applied to the sample size calculation proposal methodology for non-compartmental analysis. There were clear and drug dependent differences in calculated sample size based on various estimates of variability and overall, there was no consistent discrepancy in the sample size calculated according to the source of variability used for sample size calculations. The results are discussed in terms of their potential impact on the clinical PK studies in children. In general, considering the sensitivity of paediatric clinical PK studies and paucity of data in this group of patients, the use of p-PBPK models may offer an interim solution to uncovering age bands with potential higher vulnerability to DDI. However, these models require further refinements and testing before widely used in clinical practice with confidence.

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Книги з теми "Pharmacokinetic interactions":

1

Kiang, Tony K. L., Kyle John Wilby, and Mary H. H. Ensom. Clinical Pharmacokinetic and Pharmacodynamic Drug Interactions Associated with Antimalarials. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10527-7.

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2

Kiang, Tony K. L., Kyle John Wilby, and Mary H. H. Ensom, eds. Pharmacokinetic and Pharmacodynamic Drug Interactions Associated with Antiretroviral Drugs. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2113-8.

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3

Bartle, W. R., V. Braun, J. M. Dietschy, Y. Emori, M. Hagiwara, H. Hidaka, S. Imajoh, et al. Regulation of Plasma Low Density Lipoprotein Levels Biopharmacological Regulation of Protein Phosphorylation Calcium-Activated Neutral Protease Microbial Iron Transport Pharmacokinetic Drug Interactions. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-72902-7.

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4

David, Rodrigues A., ed. Drug-drug interactions. New York: M. Dekker, 2002.

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5

David, Rodrigues A., ed. Drug-drug interactions. 2nd ed. New York: Informa Healthcare, 2008.

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6

Huang, L. Evaluation of the potential pharmacokinetic interaction between naproxen and zidovudine. [Ottawa: Ottawa General Hospital, 1991.

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7

Calabrese, Edward J. Multiple chemical interactions. Chelsea, Mich: Lewis Publishers, 1991.

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8

Ritschel, W. A. Handbook of basic pharmacokinetics-- including clinical applications. 6th ed. Washington, D.C: American Pharmacists Association, 2004.

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9

Ritschel, W. A. Handbook of basic pharmacokinetics ... including clinical applications. 7th ed. Washington, D.C: American Pharmacists Association, 2009.

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10

Ritschel, W. A. Handbook of basic pharmacokinetics-- including clinical applications. 3rd ed. Hamilton, IL: Drug Intelligence Publications, 1986.

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Частини книг з теми "Pharmacokinetic interactions":

1

Bartle, W. R., S. E. Walker, and N. E. Winslade. "Pharmacokinetic Drug Interactions." In Progress in Clinical Biochemistry and Medicine, 101–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-72902-7_5.

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2

Wittwer, Erica D., and Wayne T. Nicholson. "Pharmacokinetic Interactions: Core Concepts." In A Case Approach to Perioperative Drug-Drug Interactions, 15–22. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-7495-1_3.

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3

Mukherjee, Biswajit. "Pharmacokinetic Drug–Drug Interactions." In Pharmacokinetics: Basics to Applications, 145–55. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8950-5_7.

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4

Renton, Kenneth W. "Cytokines and Pharmacokinetic Drug Interactions." In Methods in Pharmacology and Toxicology, 275–96. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-350-9_14.

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5

Kiang, Tony K. L., Kyle John Wilby, and Mary H. H. Ensom. "Pharmacokinetic Drug Interactions Affecting Antimalarials." In Clinical Pharmacokinetic and Pharmacodynamic Drug Interactions Associated with Antimalarials, 27–55. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10527-7_4.

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6

Markowitz, John S., and Kennerly S. Patrick. "Pharmacokinetic and Pharmacodynamic Drug Interactions." In Attention Deficit Hyperactivity Disorder, 529–50. Totowa, NJ: Humana Press, 2005. http://dx.doi.org/10.1385/1-59259-891-9:529.

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7

Huang, Shiew-Mei. "Drug-Drug Interactions." In Applications of Pharmacokinetic Principles in Drug Development, 307–31. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9216-1_10.

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8

Lewis, D. F. V. "Modelling Human Cytochrome P450-Substrate Interactions." In Pharmacokinetic Challenges in Drug Discovery, 235–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04383-7_12.

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9

Ieuter, Rachel C. "Pharmacokinetic Drug-Drug Interactions with Warfarin." In Oral Anticoagulation Therapy, 221–27. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54643-8_32.

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10

Back, D. J., and M. L’E Orme. "Pharmacokinetic Drug Interactions with Oral Contraceptives." In Steroid Contraceptives and Women’s Response, 103–23. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2445-8_10.

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Тези доповідей конференцій з теми "Pharmacokinetic interactions":

1

Moitra, Abha, Ravi Palla, Luis Tari, and Mukkai Krishnamoorthy. "Semantic Inference for Pharmacokinetic Drug-Drug Interactions." In 2014 IEEE International Conference on Semantic Computing (ICSC). IEEE, 2014. http://dx.doi.org/10.1109/icsc.2014.36.

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2

Egenlauf, Benjamin, Johanna Ohnesorge, Satenik Harutyunova, Nicola Benjamin, Christine Fischer, Yeliz Enderle, Jürgen Burhenne, et al. "Pharmacokinetic interactions in different combinations of pulmonary arterial hypertension treatment." In ERS International Congress 2016 abstracts. European Respiratory Society, 2016. http://dx.doi.org/10.1183/13993003.congress-2016.pa2397.

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3

Kulanthaivel, Palaniappan, Daruka Mahadevan, P. Kellie Turner, Jane Royalty, Wee Teck Ng, Ping Yi, Jessica Rehmel, Kenneth Cassidy, and Jill Chappell. "Abstract CT153: Pharmacokinetic drug interactions between abemaciclib and CYP3A inducers and inhibitors." In Proceedings: AACR 107th Annual Meeting 2016; April 16-20, 2016; New Orleans, LA. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1538-7445.am2016-ct153.

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4

Hunta, Sathien, and Panchit Longpradit. "Pharmacokinetic simulation for prediction of drug-drug interactions based on agent based modeling." In 2018 International Conference on Digital Arts, Media and Technology (ICDAMT). IEEE, 2018. http://dx.doi.org/10.1109/icdamt.2018.8376508.

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5

KOLCHINSKY, A., A. LOURENÇO, L. LI, and L. M. ROCHA. "EVALUATION OF LINEAR CLASSIFIERS ON ARTICLES CONTAINING PHARMACOKINETIC EVIDENCE OF DRUG-DRUG INTERACTIONS." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2012. http://dx.doi.org/10.1142/9789814447973_0040.

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6

Schneider, Elena, Patrick Hanafin, and Gauri Rao. "A retrospective observational study: Bidirectional pharmacokinetic interactions between ivacaftor-lumacaftor in cystic fibrosis." In ERS International Congress 2020 abstracts. European Respiratory Society, 2020. http://dx.doi.org/10.1183/13993003.congress-2020.362.

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7

Pawaskar, Dipti K., Robert Straubinger, Gerald Fetterly, Wen Ma, and William Jusko. "Abstract 27: Physiologically based pharmacokinetic model for interactions of sorafenib and everolimus in mice." In Proceedings: AACR 102nd Annual Meeting 2011‐‐ Apr 2‐6, 2011; Orlando, FL. American Association for Cancer Research, 2011. http://dx.doi.org/10.1158/1538-7445.am2011-27.

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8

Yang, Xiaoxia, Hofmeister C. Craig, Darlene M. Rozewski, Seungsoo Lee, Ping Chen, Amy J. Johnson, Zhongfa Liu, et al. "Abstract 5473: The contribution of P-glycoprotein to clinical pharmacokinetic interactions between lenalidomide and temsirolimus." In Proceedings: AACR 102nd Annual Meeting 2011‐‐ Apr 2‐6, 2011; Orlando, FL. American Association for Cancer Research, 2011. http://dx.doi.org/10.1158/1538-7445.am2011-5473.

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9

Sidharta, P. N., P. L. M. van Giersbergen, Michael Wolzt, and Jasper Dingemanse. "Lack Of Clinically Relevant Pharmacokinetic Interactions Between The Dual Endothelin Receptor Antagonist Macitentan And Sildenafil In Healthy Subjects." In American Thoracic Society 2012 International Conference, May 18-23, 2012 • San Francisco, California. American Thoracic Society, 2012. http://dx.doi.org/10.1164/ajrccm-conference.2012.185.1_meetingabstracts.a4802.

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10

Georges, G., A. Lucardie, C. Garofalo, C. Beaudot, and M. Cella. "Pharmacokinetic/Pharmacodynamic Interactions Between Extrafine Beclomethasone Dipropionate and Formoterol Fumarate Components of a Fixed-Dose Combination for Asthma and COPD." In American Thoracic Society 2019 International Conference, May 17-22, 2019 - Dallas, TX. American Thoracic Society, 2019. http://dx.doi.org/10.1164/ajrccm-conference.2019.199.1_meetingabstracts.a4528.

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