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Viviani R, Berres J, Stingl JC. Phenotypic Models of Drug-Drug-Gene Interactions Mediated by Cytochrome Drug-Metabolizing Enzymes. Clin Pharmacol Ther 2024; 116:592-601. [PMID: 38318716 DOI: 10.1002/cpt.3188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 01/15/2024] [Indexed: 02/07/2024]
Abstract
Genetic polymorphisms in drug metabolizing enzymes and drug-drug interactions are major sources of inadequate drug exposure and ensuing adverse effects or insufficient responses. The current challenge in assessing drug-drug gene interactions (DDGIs) for the development of precise dose adjustment recommendation systems is to take into account both simultaneously. Here, we analyze the static models of DDGI from in vivo data and focus on the concept of phenoconversion to model inhibition and genetic polymorphisms jointly. These models are applicable to datasets where pharmacokinetic information is missing and are being used in clinical support systems and consensus dose adjustment guidelines. We show that all such models can be handled by the same formal framework, and that models that differ at first sight are all versions of the same linear phenoconversion model. This model includes the linear pharmacogenetic and inhibition models as special cases. We highlight present challenges in this endeavor and the open issues for future research in developing DDGI models for recommendation systems.
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Affiliation(s)
- Roberto Viviani
- Institute of Psychology, University of Innsbruck, Innsbruck, Austria
- Department of Psychiatry and Psychotherapy III, University of Ulm, Ulm, Germany
| | - Judith Berres
- Institute of Clinical Pharmacology, University Hospital RWTH Aachen, Aachen, Germany
| | - Julia C Stingl
- Institute of Clinical Pharmacology, University Hospital RWTH Aachen, Aachen, Germany
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2
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Mostafa S, Polasek TM, Bousman C, Rostami‐Hodjegan A, Sheffield LJ, Everall I, Pantelis C, Kirkpatrick CMJ. Delineating gene-environment effects using virtual twins of patients treated with clozapine. CPT Pharmacometrics Syst Pharmacol 2022; 12:168-179. [PMID: 36424701 PMCID: PMC9931435 DOI: 10.1002/psp4.12886] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 10/11/2022] [Accepted: 10/13/2022] [Indexed: 11/27/2022] Open
Abstract
Studies that focus on individual covariates, while ignoring their interactions, may not be adequate for model-informed precision dosing (MIPD) in any given patient. Genetic variations that influence protein synthesis should be studied in conjunction with environmental covariates, such as cigarette smoking. The aim of this study was to build virtual twins (VTs) of real patients receiving clozapine with interacting covariates related to genetics and environment and to delineate the impact of interacting covariates on predicted clozapine plasma concentrations. Clozapine-treated patients with schizophrenia (N = 42) with observed clozapine plasma concentrations, demographic, environmental, and genotype data were used to construct VTs in Simcyp. The effect of increased covariate virtualization was assessed by performing simulations under three conditions: "low" (demographic), "medium" (demographic and environmental interaction), and "high" (demographic and environmental/genotype interaction) covariate virtualization. Increasing covariate virtualization with interaction improved the coefficient of variation (R2 ) from 0.07 in the low model to 0.391 and 0.368 in the medium and high models, respectively. Whereas R2 was similar between the medium and high models, the high covariate virtualization model had improved accuracy, with systematic bias of predicted clozapine plasma concentration improving from -138.48 ng/ml to -74.65 ng/ml. A high level of covariate virtualization (demographic, environmental, and genotype) may be required for MIPD using VTs.
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Affiliation(s)
- Sam Mostafa
- Centre for Medicine Use and SafetyMonash UniversityVictoriaParkvilleAustralia,MyDNA LifeAustralia LimitedVictoriaSouth YarraAustralia
| | - Thomas M. Polasek
- Centre for Medicine Use and SafetyMonash UniversityVictoriaParkvilleAustralia,CertaraNew JerseyPrincetonUSA,Department of Clinical PharmacologyRoyal Adelaide HospitalSouth AustraliaAdelaideAustralia
| | - Chad Bousman
- Melbourne Neuropsychiatry Centre, Department of PsychiatryUniversity of Melbourne & Melbourne HealthVictoriaMelbourneAustralia,The Cooperative Research Centre (CRC) for Mental HealthVictoriaMelbourneAustralia,Alberta Children's Hospital Research Institute, Cumming School of MedicineUniversity of CalgaryAlbertaCalgaryCanada,Hotchkiss Brain Institute, Cumming School of MedicineUniversity of CalgaryAlbertaCalgaryCanada,Departments of Medical Genetics, Psychiatry, and Physiology and PharmacologyUniversity of CalgaryAlbertaCalgaryCanada
| | - Amin Rostami‐Hodjegan
- Centre for Applied Pharmacokinetic Research (CAPKR), School of Health SciencesUniversity of ManchesterManchesterUK,Simcyp DivisionCertara UK LimitedSheffieldUK
| | | | - Ian Everall
- Melbourne Neuropsychiatry Centre, Department of PsychiatryUniversity of Melbourne & Melbourne HealthVictoriaMelbourneAustralia,The Cooperative Research Centre (CRC) for Mental HealthVictoriaMelbourneAustralia,Western Australian Health Translation NetworkNedlandsWestern AustraliaAustralia,Florey Institute of Neuroscience and Mental HealthUniversity of MelbourneVictoriaMelbourneAustralia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of PsychiatryUniversity of Melbourne & Melbourne HealthVictoriaMelbourneAustralia,The Cooperative Research Centre (CRC) for Mental HealthVictoriaMelbourneAustralia,Florey Institute of Neuroscience and Mental HealthUniversity of MelbourneVictoriaMelbourneAustralia
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3
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Grzegorzewski J, Brandhorst J, König M. Physiologically based pharmacokinetic (PBPK) modeling of the role of CYP2D6 polymorphism for metabolic phenotyping with dextromethorphan. Front Pharmacol 2022; 13:1029073. [PMID: 36353484 PMCID: PMC9637881 DOI: 10.3389/fphar.2022.1029073] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 09/23/2022] [Indexed: 11/24/2022] Open
Abstract
The cytochrome P450 2D6 (CYP2D6) is a key xenobiotic-metabolizing enzyme involved in the clearance of many drugs. Genetic polymorphisms in CYP2D6 contribute to the large inter-individual variability in drug metabolism and could affect metabolic phenotyping of CYP2D6 probe substances such as dextromethorphan (DXM). To study this question, we (i) established an extensive pharmacokinetics dataset for DXM; and (ii) developed and validated a physiologically based pharmacokinetic (PBPK) model of DXM and its metabolites dextrorphan (DXO) and dextrorphan O-glucuronide (DXO-Glu) based on the data. Drug-gene interactions (DGI) were introduced by accounting for changes in CYP2D6 enzyme kinetics depending on activity score (AS), which in combination with AS for individual polymorphisms allowed us to model CYP2D6 gene variants. Variability in CYP3A4 and CYP2D6 activity was modeled based on in vitro data from human liver microsomes. Model predictions are in very good agreement with pharmacokinetics data for CYP2D6 polymorphisms, CYP2D6 activity as described by the AS system, and CYP2D6 metabolic phenotypes (UM, EM, IM, PM). The model was applied to investigate the genotype-phenotype association and the role of CYP2D6 polymorphisms for metabolic phenotyping using the urinary cumulative metabolic ratio (UCMR), DXM/(DXO + DXO-Glu). The effect of parameters on UCMR was studied via sensitivity analysis. Model predictions indicate very good robustness against the intervention protocol (i.e. application form, dosing amount, dissolution rate, and sampling time) and good robustness against physiological variation. The model is capable of estimating the UCMR dispersion within and across populations depending on activity scores. Moreover, the distribution of UCMR and the risk of genotype-phenotype mismatch could be estimated for populations with known CYP2D6 genotype frequencies. The model can be applied for individual prediction of UCMR and metabolic phenotype based on CYP2D6 genotype. Both, model and database are freely available for reuse.
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4
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Brown KE, Staples JW, Woodahl EL. Keeping pace with CYP2D6 haplotype discovery: innovative methods to assign function. Pharmacogenomics 2022; 23:255-262. [PMID: 35083931 PMCID: PMC8890136 DOI: 10.2217/pgs-2021-0149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The discovery of haplotypes with unknown or uncertain function in the CYP2D6 pharmacogene is outpacing the capabilities of traditional in vitro and in vivo approaches to characterize their function. This challenge will undoubtedly grow as pharmacogenomic research becomes more inclusive of globally diverse populations. As accurate phenotypic assignment is paramount to the utility of pharmacogenomics, high-throughput technologies are needed for this complex pharmacogene. We describe the evolving landscape of innovative approaches to assign function to CYP2D6 haplotypes and possibilities for adopting these technologies into cohesive processes. Promising approaches include ADME-optimized prediction frameworks, machine learning algorithms, deep mutational scanning and phenoconversion predictions. Implementing these approaches will lead to improved personalization of treatment for patients.
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Affiliation(s)
- Karen E Brown
- Department of Biomedical & Pharmaceutical Sciences, Skaggs School of Pharmacy, University of Montana, Missoula, MT 59812, USA,Skaggs Institute for Health Innovation, University of Montana, Missoula, MT 59812, USA
| | - Jack W Staples
- Department of Biomedical & Pharmaceutical Sciences, Skaggs School of Pharmacy, University of Montana, Missoula, MT 59812, USA,Skaggs Institute for Health Innovation, University of Montana, Missoula, MT 59812, USA
| | - Erica L Woodahl
- Department of Biomedical & Pharmaceutical Sciences, Skaggs School of Pharmacy, University of Montana, Missoula, MT 59812, USA,Skaggs Institute for Health Innovation, University of Montana, Missoula, MT 59812, USA,Author for correspondence: Tel.: +1 406 243 4129;
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5
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Mifsud Buhagiar L, Casha M, Grech A, Serracino Inglott A, LaFerla G. The interplay between pharmacogenetics, concomitant drugs and blood levels of amitriptyline and its main metabolites. Per Med 2022; 19:113-123. [PMID: 35118877 DOI: 10.2217/pme-2021-0022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Background: The research considers the impact of genotype-inferred variability on blood levels of amitriptyline and its main metabolites, as may be moderated by phenocopying. Patients & methods: CYP2D6 and CYP2C19 genotypes, and serum concentrations of amitriptyline, nortriptyline and hydroxymetabolites, were determined in 33 outpatients. Co-medications were reviewed to identify CYP inhibition risk. Results: CYP2C19 metabolizer status explained interpatient variation in nortriptyline to amitriptyline concentration ratios. The hydroxymetabolite to parent ratios increased with higher CYP2D6 activity scores and lower CYP2D6 inhibition risk. In patients at high CYP2D6 inhibition risk, the amitriptyline + nortriptyline concentration was, on average, 52% above the higher end of expected ranges. Conclusion: Practical construal of pharmacogenetics and drug interactions tantamount to aberrant metabolism can facilitate patient-tailored use of the established drug.
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Affiliation(s)
- Luana Mifsud Buhagiar
- Department of Pharmacy, Faculty of Medicine & Surgery, University of Malta, Msida, MSD 2080, Malta.,Medicines Authority, Malta Life Sciences Park, San Ġwann, SĠN 3000, Malta
| | - Marilyn Casha
- Department of Surgery, Faculty of Medicine & Surgery, University of Malta, Msida, MSD 2080, Malta
| | - Anton Grech
- Department of Psychiatry, Faculty of Medicine & Surgery, University of Malta, Msida, MSD 2080, Malta
| | - Anthony Serracino Inglott
- Department of Pharmacy, Faculty of Medicine & Surgery, University of Malta, Msida, MSD 2080, Malta.,Medicines Authority, Malta Life Sciences Park, San Ġwann, SĠN 3000, Malta
| | - Godfrey LaFerla
- Department of Surgery, Faculty of Medicine & Surgery, University of Malta, Msida, MSD 2080, Malta
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Severe CNS depression with duloxetine, ciprofloxacin and CYP2D6 deficiency-role and recognition of drug-drug-gene interactions. Eur J Clin Pharmacol 2022; 78:703-705. [PMID: 35039909 DOI: 10.1007/s00228-022-03278-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 01/12/2022] [Indexed: 12/22/2022]
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Abouir K, Samer CF, Gloor Y, Desmeules JA, Daali Y. Reviewing Data Integrated for PBPK Model Development to Predict Metabolic Drug-Drug Interactions: Shifting Perspectives and Emerging Trends. Front Pharmacol 2021; 12:708299. [PMID: 34776945 PMCID: PMC8582169 DOI: 10.3389/fphar.2021.708299] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 10/18/2021] [Indexed: 01/03/2023] Open
Abstract
Physiologically-based pharmacokinetics (PBPK) modeling is a robust tool that supports drug development and the pharmaceutical industry and regulatory authorities. Implementation of predictive systems in the clinics is more than ever a reality, resulting in a surge of interest for PBPK models by clinicians. We aimed to establish a repository of available PBPK models developed to date to predict drug-drug interactions (DDIs) in the different therapeutic areas by integrating intrinsic and extrinsic factors such as genetic polymorphisms of the cytochromes or environmental clues. This work includes peer-reviewed publications and models developed in the literature from October 2017 to January 2021. Information about the software, type of model, size, and population model was extracted for each article. In general, modeling was mainly done for DDI prediction via Simcyp® software and Full PBPK. Overall, the necessary physiological and physio-pathological parameters, such as weight, BMI, liver or kidney function, relative to the drug absorption, distribution, metabolism, and elimination and to the population studied for model construction was publicly available. Of the 46 articles, 32 sensibly predicted DDI potentials, but only 23% integrated the genetic aspect to the developed models. Marked differences in concentration time profiles and maximum plasma concentration could be explained by the significant precision of the input parameters such as Tissue: plasma partition coefficients, protein abundance, or Ki values. In conclusion, the models show a good correlation between the predicted and observed plasma concentration values. These correlations are all the more pronounced as the model is rich in data representative of the population and the molecule in question. PBPK for DDI prediction is a promising approach in clinical, and harmonization of clearance prediction may be helped by a consensus on selecting the best data to use for PBPK model development.
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Affiliation(s)
- Kenza Abouir
- Division of Clinical Pharmacology and Toxicology, Department of Anesthesiology, Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland.,Institute of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, Geneva, Switzerland
| | - Caroline F Samer
- Division of Clinical Pharmacology and Toxicology, Department of Anesthesiology, Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland.,Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Yvonne Gloor
- Division of Clinical Pharmacology and Toxicology, Department of Anesthesiology, Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Jules A Desmeules
- Division of Clinical Pharmacology and Toxicology, Department of Anesthesiology, Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland.,Institute of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, Geneva, Switzerland.,Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Youssef Daali
- Division of Clinical Pharmacology and Toxicology, Department of Anesthesiology, Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland.,Institute of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, Geneva, Switzerland.,Faculty of Medicine, University of Geneva, Geneva, Switzerland
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8
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Jeong HC, Chae YJ, Lee S, Kang W, Yun HY, Shin KH. Prediction of Fluoxetine and Norfluoxetine Pharmacokinetic Profiles Using Physiologically Based Pharmacokinetic Modeling. J Clin Pharmacol 2021; 61:1505-1513. [PMID: 34118174 DOI: 10.1002/jcph.1927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 06/10/2021] [Indexed: 11/06/2022]
Abstract
Fluoxetine is a selective serotonin reuptake inhibitor that is metabolized to norfluoxetine by cytochrome P450 (CYP) 2D6, CYP2C19, and CYP3A4. A physiologically based pharmacokinetic model for fluoxetine and norfluoxetine metabolism was developed to predict and investigate changes in concentration-time profiles according to fluoxetine dosage in the Korean population. The model was developed based on the Certara repository model and information gleaned from the literature. Digitally extracted clinical study data were used to develop and verify the model. Simulations for plasma concentrations of fluoxetine and norfluoxetine after a single dose of 60 or 80 mg fluoxetine were made based on 1000 virtual healthy Korean individuals using the SimCYP version 19 simulator. The mean ratios (simulated/observed) after a single administration of 80 mg fluoxetine for maximum plasma concentration, area under the plasma concentration-time curve, and apparent clearance were 1.12, 1.08, and 0.93 for fluoxetine; the ratios of maximum plasma concentration and area under the plasma concentration-time curve were 1.08 and 1.08, respectively, for norfluoxetine, indicating that the simulated concentration-time profiles of fluoxetine and norfluoxetine fitted the observed profiles well. The developed model was used to predict plasma fluoxetine and norfluoxetine concentration-time profiles after repeated administrations of fluoxetine in Korean volunteers. This physiologically based pharmacokinetic model could provide basic understanding of the pharmacokinetic profiles of fluoxetine and its metabolite under various situations.
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Affiliation(s)
- Hyeon-Cheol Jeong
- College of Pharmacy, Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu, Republic of Korea
| | - Yoon-Jee Chae
- College of Pharmacy, Woosuk University, Jeonbuk, Republic of Korea
| | - Sooyeun Lee
- College of Pharmacy, Keimyung University, Daegu, Republic of Korea
| | - Wonku Kang
- College of Pharmacy, Chung-Ang University, Seoul, Republic of Korea
| | - Hwi-Yeol Yun
- College of Pharmacy, Chungnam National University, Daejeon, Republic of Korea
| | - Kwang-Hee Shin
- College of Pharmacy, Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu, Republic of Korea
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9
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Xu M, Zheng L, Zeng J, Xu W, Jiang X, Wang L. Physiologically based pharmacokinetic modeling of tramadol to inform dose adjustment and drug-drug interactions according to CYP2D6 phenotypes. Pharmacotherapy 2021; 41:277-290. [PMID: 33316842 DOI: 10.1002/phar.2494] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/06/2020] [Accepted: 11/06/2020] [Indexed: 12/23/2022]
Abstract
OBJECTIVES The objective of this study was to establish physiologically based pharmacokinetic (PBPK) models of tramadol and its active metabolite O-desmethyltramadol (M1) and to explore the influence of CYP2D6 gene polymorphism on the pharmacokinetics of tramadol and M1. Furthermore, we used PBPK modeling to prospectively predict the extent of drug-drug interactions (DDIs) in the presence of genetic polymorphisms when tramadol was co-administered with the CYP2D6 inhibitors duloxetine and paroxetine. METHODS Plasma concentrations of tramadol and M1 were used to adjust the turnover frequency (Kcat ) of CYP2D6 for phenotype populations with different CYP2D6 genotypes. PBPK models were developed to capture the pharmacokinetics between CYP2D6 extensive metabolizers (EMs), intermediate metabolizers (IMs), poor metabolizers (PMs), and ultra-rapid metabolizers (UMs). The validated models were then used to support dose adjustment in different CYP2D6 phenotypes and to predict the extent of CYP2D6-mediated DDIs when tramadol was co-administered with paroxetine or duloxetine. RESULTS The PBPK models we built accurately describe tramadol and M1 exposure in the population with different CYP2D6 phenotypes. In our prediction, the area under the concentration-time curve (AUCinf-tDlast ) of M1 is 70% lower in PMs than in EMs, 27% lower in IMs, and 15% higher in UMs. Based on the models we built, we suggest that the oral dose of tramadol should be 50% higher for IMs and 25% lower for UMs to achieve an approximately equivalent plasma exposure of M1 as in EMs. When tramadol was co-administered with paroxetine or duloxetine, the magnitude of the inhibitor-substrate interaction was lowest in EMs (0.45), secondary in IMs (0.39), and highest in PMs (0.18) in terms of M1. CONCLUSION The current example uses the PBPK model to guide dose adjustment of tramadol and to predict the effect of CYP2D6 genetic polymorphisms on DDIs for rational clinical use of tramadol in the future.
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Affiliation(s)
- Miao Xu
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - Liang Zheng
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - Jin Zeng
- Department of Pharmacy, Chongqing University Cancer Hospital, Chongqing, China
| | - Wenwen Xu
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - Xuehua Jiang
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - Ling Wang
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
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10
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Abstract
Accurate estimation of in vivo clearance in human is pivotal to determine the dose and dosing regimen for drug development. In vitro-in vivo extrapolation (IVIVE) has been performed to predict drug clearance using empirical and physiological scalars. Multiple in vitro systems and mathematical modeling techniques have been employed to estimate in vivo clearance. The models for predicting clearance have significantly improved and have evolved to become more complex by integrating multiple processes such as drug metabolism and transport as well as passive diffusion. This chapter covers the use of conventional as well as recently developed methods to predict metabolic and transporter-mediated clearance along with the advantages and disadvantages of using these methods and the associated experimental considerations. The general approaches to improve IVIVE by use of appropriate scalars, incorporation of extrahepatic metabolism and transport and application of physiologically based pharmacokinetic (PBPK) models with proteomics data are also discussed. The chapter also provides an overview of the advantages of using such dynamic mechanistic models over static models for clearance predictions to improve IVIVE.
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11
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Mostafa S, Polasek TM, Sheffield LJ, Huppert D, Kirkpatrick CMJ. Quantifying the Impact of Phenoconversion on Medications With Actionable Pharmacogenomic Guideline Recommendations in an Acute Aged Persons Mental Health Setting. Front Psychiatry 2021; 12:724170. [PMID: 34489765 PMCID: PMC8416898 DOI: 10.3389/fpsyt.2021.724170] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 07/27/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: Polypharmacy and genetic variants that strongly influence medication response (pharmacogenomics, PGx) are two well-described risk factors for adverse drug reactions. Complexities arise in interpreting PGx results in the presence of co-administered medications that can cause cytochrome P450 enzyme phenoconversion. Aim: To quantify phenoconversion in a cohort of acute aged persons mental health patients and evaluate its impact on the reporting of medications with actionable PGx guideline recommendations (APRs). Methods: Acute aged persons mental health patients (N = 137) with PGx and medication data at admission and discharge were selected to describe phenoconversion frequencies for CYP2D6, CYP2C19 and CYP2C9 enzymes. The expected impact of phenoconversion was then assessed on the reporting of medications with APRs. Results: Post-phenoconversion, the predicted frequency at admission and discharge increased for CYP2D6 intermediate metabolisers (IMs) by 11.7 and 16.1%, respectively. Similarly, for CYP2C19 IMs, the predicted frequency at admission and discharge increased by 13.1 and 11.7%, respectively. Nineteen medications with APRs were prescribed 120 times at admission, of which 50 (42%) had APRs pre-phenoconversion, increasing to 60 prescriptions (50%) post-phenoconversion. At discharge, 18 medications with APRs were prescribed 122 times, of which 48 (39%) had APRs pre-phenoconversion, increasing to 57 prescriptions (47%) post-phenoconversion. Discussion: Aged persons mental health patients are commonly prescribed medications with APRs, but interpretation of these recommendations must consider the effects of phenoconversion. Adopting a collaborative care model between prescribers and clinical pharmacists should be considered to address phenoconversion and ensure the potential benefits of PGx are maximised.
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Affiliation(s)
- Sam Mostafa
- Centre for Medicine Use and Safety, Monash University, Parkville, VIC, Australia.,MyDNA Life, Australia Limited, South Yarra, VIC, Australia
| | - Thomas M Polasek
- Centre for Medicine Use and Safety, Monash University, Parkville, VIC, Australia.,Certara, Princeton, NJ, United States.,Department of Clinical Pharmacology, Royal Adelaide Hospital, Adelaide, SA, Australia
| | - Leslie J Sheffield
- MyDNA Life, Australia Limited, South Yarra, VIC, Australia.,Department of Genetic Medicine, Melbourne Health, Parkville, VIC, Australia
| | - David Huppert
- Department of Aged & Liaison Psychiatry, Alfred Health, Melbourne, VIC, Australia.,Northwestern Mental Health, Melbourne Health, Melbourne, VIC, Australia
| | - Carl M J Kirkpatrick
- Centre for Medicine Use and Safety, Monash University, Parkville, VIC, Australia
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12
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Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling to Predict the Impact of CYP2C9 Genetic Polymorphisms, Co-Medication and Formulation on the Pharmacokinetics and Pharmacodynamics of Flurbiprofen. Pharmaceutics 2020; 12:pharmaceutics12111049. [PMID: 33147873 PMCID: PMC7693160 DOI: 10.3390/pharmaceutics12111049] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 10/23/2020] [Accepted: 10/26/2020] [Indexed: 02/01/2023] Open
Abstract
Physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models can serve as a powerful framework for predicting the influence as well as the interaction of formulation, genetic polymorphism and co-medication on the pharmacokinetics and pharmacodynamics of drug substances. In this study, flurbiprofen, a potent non-steroid anti-inflammatory drug, was chosen as a model drug. Flurbiprofen has absolute bioavailability of ~95% and linear pharmacokinetics in the dose range of 50–300 mg. Its absorption is considered variable and complex, often associated with double peak phenomena, and its pharmacokinetics are characterized by high inter-subject variability, mainly due to its metabolism by the polymorphic CYP2C9 (fmCYP2C9 ≥ 0.71). In this study, by leveraging in vitro, in silico and in vivo data, an integrated PBPK/PD model with mechanistic absorption was developed and evaluated against clinical data from PK, PD, drug-drug and gene-drug interaction studies. The PBPK model successfully predicted (within 2-fold) 36 out of 38 observed concentration-time profiles of flurbiprofen as well as the CYP2C9 genetic effects after administration of different intravenous and oral dosage forms over a dose range of 40–300 mg in both Caucasian and Chinese healthy volunteers. All model predictions for Cmax, AUCinf and CL/F were within two-fold of their respective mean or geometric mean values, while 90% of the predictions of Cmax, 81% of the predictions of AUCinf and 74% of the predictions of Cl/F were within 1.25 fold. In addition, the drug-drug and drug-gene interactions were predicted within 1.5-fold of the observed interaction ratios (AUC, Cmax ratios). The validated PBPK model was further expanded by linking it to an inhibitory Emax model describing the analgesic efficacy of flurbiprofen and applying it to explore the effect of formulation and genetic polymorphisms on the onset and duration of pain relief. This comprehensive PBPK/PD analysis, along with a detailed translational biopharmaceutic framework including appropriately designed biorelevant in vitro experiments and in vitro-in vivo extrapolation, provided mechanistic insight on the impact of formulation and genetic variations, two major determinants of the population variability, on the PK/PD of flurbiprofen. Clinically relevant specifications and potential dose adjustments were also proposed. Overall, the present work highlights the value of a translational PBPK/PD approach, tailored to target populations and genotypes, as an approach towards achieving personalized medicine.
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Suntsov V, Jovanovic F, Knezevic E, Candido KD, Knezevic NN. Can Implementation of Genetics and Pharmacogenomics Improve Treatment of Chronic Low Back Pain? Pharmaceutics 2020; 12:pharmaceutics12090894. [PMID: 32967120 PMCID: PMC7558486 DOI: 10.3390/pharmaceutics12090894] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/11/2020] [Accepted: 09/14/2020] [Indexed: 12/17/2022] Open
Abstract
Etiology of back pain is multifactorial and not completely understood, and for the majority of people who suffer from chronic low back pain (cLBP), the precise cause cannot be determined. We know that back pain is somewhat heritable, chronic pain more so than acute. The aim of this review is to compile the genes identified by numerous genetic association studies of chronic pain conditions, focusing on cLBP specifically. Higher-order neurologic processes involved in pain maintenance and generation may explain genetic contributions and functional predisposition to formation of cLBP that does not involve spine pathology. Several genes have been identified in genetic association studies of cLBP and roughly, these genes could be grouped into several categories, coding for: receptors, enzymes, cytokines and related molecules, and transcription factors. Treatment of cLBP should be multimodal. In this review, we discuss how an individual's genotype could affect their response to therapy, as well as how genetic polymorphisms in CYP450 and other enzymes are crucial for affecting the metabolic profile of drugs used for the treatment of cLBP. Implementation of gene-focused pharmacotherapy has the potential to deliver select, more efficacious drugs and avoid unnecessary, polypharmacy-related adverse events in many painful conditions, including cLBP.
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Affiliation(s)
- Vladislav Suntsov
- Department of Anesthesiology, Advocate Illinois Masonic Medical Center, 836 W. Wellington Ave. Suite 4815, Chicago, IL 60657, USA; (V.S.); (F.J.); (E.K.); (K.D.C.)
| | - Filip Jovanovic
- Department of Anesthesiology, Advocate Illinois Masonic Medical Center, 836 W. Wellington Ave. Suite 4815, Chicago, IL 60657, USA; (V.S.); (F.J.); (E.K.); (K.D.C.)
| | - Emilija Knezevic
- Department of Anesthesiology, Advocate Illinois Masonic Medical Center, 836 W. Wellington Ave. Suite 4815, Chicago, IL 60657, USA; (V.S.); (F.J.); (E.K.); (K.D.C.)
| | - Kenneth D. Candido
- Department of Anesthesiology, Advocate Illinois Masonic Medical Center, 836 W. Wellington Ave. Suite 4815, Chicago, IL 60657, USA; (V.S.); (F.J.); (E.K.); (K.D.C.)
- Department of Anesthesiology, University of Illinois, Chicago, IL 60612, USA
- Department of Surgery, University of Illinois, Chicago, IL 60612, USA
| | - Nebojsa Nick Knezevic
- Department of Anesthesiology, Advocate Illinois Masonic Medical Center, 836 W. Wellington Ave. Suite 4815, Chicago, IL 60657, USA; (V.S.); (F.J.); (E.K.); (K.D.C.)
- Department of Anesthesiology, University of Illinois, Chicago, IL 60612, USA
- Department of Surgery, University of Illinois, Chicago, IL 60612, USA
- Correspondence: ; Tel.: +1-773-296-5619; Fax: +1-773-296-5362
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Cicali EJ, Smith DM, Duong BQ, Kovar LG, Cavallari LH, Johnson JA. A Scoping Review of the Evidence Behind Cytochrome P450 2D6 Isoenzyme Inhibitor Classifications. Clin Pharmacol Ther 2020; 108:116-125. [PMID: 31910286 DOI: 10.1002/cpt.1768] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 12/16/2019] [Indexed: 12/12/2022]
Abstract
The US Food and Drug Administration (FDA) lists 22 medications as clinical inhibitors of cytochrome P450 2D6 isoenzyme, with classifications of strong, moderate, and weak. It is accepted that strong inhibitors result in nearly null enzymatic activity, but reduction caused by moderate and weak inhibitors is less well characterized. The objective was to identify if the classification of currently listed FDA moderate and weak inhibitors is supported by publicly available primary literature. We conducted a literature search and reviewed product labels for area under the plasma concentration-time curve (AUC) fold-changes caused by inhibitors in humans and identified 89 inhibitor-substrate pairs. Observed AUC fold-change of the substrate was used to create an observed inhibitor classification per FDA-defined AUC fold-change thresholds. We then compared the observed inhibitor classification with the classification listed in the FDA Table of Inhibitors. We found 62% of the inhibitors within the pairs matched the listed FDA classification. We explored reasons for discordance and suggest modifications to the FDA table of clinical inhibitors for cimetidine, desvenlafaxine, and fluvoxamine.
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Affiliation(s)
- Emily J Cicali
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, University of Florida College of Pharmacy, Gainesville, Florida, USA
| | - D Max Smith
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, University of Florida College of Pharmacy, Gainesville, Florida, USA
| | - Benjamin Q Duong
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, University of Florida College of Pharmacy, Gainesville, Florida, USA
| | - Lukas G Kovar
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, University of Florida College of Pharmacy, Gainesville, Florida, USA
| | - Larisa H Cavallari
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, University of Florida College of Pharmacy, Gainesville, Florida, USA
| | - Julie A Johnson
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, University of Florida College of Pharmacy, Gainesville, Florida, USA
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