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Yadav J, Maldonato BJ, Roesner JM, Vergara AG, Paragas EM, Aliwarga T, Humphreys S. Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic. Drug Metab Rev 2024:1-33. [PMID: 39057923 DOI: 10.1080/03602532.2024.2381021] [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: 02/23/2024] [Accepted: 07/12/2024] [Indexed: 07/28/2024]
Abstract
Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.
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Affiliation(s)
- Jaydeep Yadav
- Department of Pharmacokinetics, Dynamics, Metabolism & Bioanalytics (PDMB), Merck & Co., Inc., Boston, MA, USA
| | - Benjamin J Maldonato
- Department of Nonclinical Development and Clinical Pharmacology, Revolution Medicines, Inc., Redwood City, CA, USA
| | - Joseph M Roesner
- Department of Pharmacokinetics, Dynamics, Metabolism & Bioanalytics (PDMB), Merck & Co., Inc., Boston, MA, USA
| | - Ana G Vergara
- Department of Pharmacokinetics, Dynamics, Metabolism & Bioanalytics (PDMB), Merck & Co., Inc., Rahway, NJ, USA
| | - Erickson M Paragas
- Pharmacokinetics and Drug Metabolism Department, Amgen Research, South San Francisco, CA, USA
| | - Theresa Aliwarga
- Pharmacokinetics and Drug Metabolism Department, Amgen Research, South San Francisco, CA, USA
| | - Sara Humphreys
- Pharmacokinetics and Drug Metabolism Department, Amgen Research, South San Francisco, CA, USA
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2
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Xiao R, Fu Q, Chen L, Li T, Du X. Development and Validation of a High-Performance Liquid Chromatography Coupled With Ultraviolet Detection Method for Quantification of Bictegravir in Human Plasma. Ther Drug Monit 2024:00007691-990000000-00242. [PMID: 38917376 DOI: 10.1097/ftd.0000000000001235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Accepted: 05/01/2024] [Indexed: 06/27/2024]
Abstract
BACKGROUND To establish a method for determining the bictegravir (BIC) concentration in human plasma using high-performance liquid chromatography coupled with ultraviolet detection. METHODS The analysis was performed on a CLC-octadecylsilane column (150 × 6.0 mm, 5 μm) using a mixture of phosphate buffer and acetonitrile (62:38, v/v) as the mobile phase at the flow rate of 1.4 mL/min. The column temperature was maintained at 40°C. Using triamcinolone acetonide as the internal standard, 100 μL of plasma sample was extracted by methyl tert-butyl ether, followed by evaporating under nitrogen stream, redissolving with 100 μL mobile phase, and injection of 20-40 μL of supernatant into the chromatographic system. Ultraviolet detection was performed at 260 nm, and the total run time for each sample was 14 minutes. RESULTS The method exhibited good linearity within the range from 0.10 to 10.0 mcg/mL (r = 0.9995, n = 5). The intraday and interday relative standard deviations for low-, medium-, and high-concentration quality control samples (0.20, 4.00, 8.00 mcg/mL) and the lower limit of quantification (0.10 mcg/mL) were 1.31%-6.20% (n = 10) and 1.18%-2.87% (n = 5), respectively. The intraday and interday accuracies were 100.53%-102.32% and 97.96%-103.84%, respectively. The extraction recovery rates ranged from 80.00% to 88.09% (n = 3). The stability tests showed that the BIC concentration changed by <15%. CONCLUSIONS This study successfully established a high-performance liquid chromatography coupled with ultraviolet detection method for determining plasma BIC concentrations. This method is simple, selective, sensitive, and accurate, making it suitable for clinical monitoring and pharmacokinetic studies of BIC.
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Affiliation(s)
- Ran Xiao
- Department of Pharmacy, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Qiang Fu
- Department of Pharmacy, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Ling Chen
- Department of Infectious Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Taisheng Li
- Department of Infectious Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- School of Medicine, Tsinghua University, Beijing, China; and
- State Key Laboratory for Complex, Severe, and Rare Diseases, Peking Union Medical College Hospital, Beijing, China
| | - Xiaoli Du
- Department of Pharmacy, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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3
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Wang X, Chen F, Guo N, Gu Z, Lin H, Xiang X, Shi Y, Han B. Application of physiologically based pharmacokinetics modeling in the research of small-molecule targeted anti-cancer drugs. Cancer Chemother Pharmacol 2023; 92:253-270. [PMID: 37466731 DOI: 10.1007/s00280-023-04566-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 07/05/2023] [Indexed: 07/20/2023]
Abstract
INTRODUCTION Physiologically based pharmacokinetics (PBPK) models are increasingly used in the drug research and development, especially in anti-cancer drugs. Between 2001 and 2020, a total of 89 small-molecule targeted antitumor drugs were approved in China and the United States, some of which already included PBPK modeling in their application or approval packages. This article intended to review the prevalence and application of PBPK model in these drugs. METHOD Article search was performed in the PubMed to collect English research articles on small-molecule targeted anti-cancer drugs using PBPK modeling. The selected articles were classified into nine categorizes according to the application areas and further analyzed. RESULT From 2001 to 2020, more than 60% of small-molecule targeted anti-cancer drugs (54/89) were studied using PBPK model with a wide range of application. Ninety research articles were included, of which 48 involved enzyme-mediated drug-drug interaction (DDI). Of these retrieved articles, Simcyp, GastroPlus, and PK-Sim were the most widely model building platforms, which account for 63.8%, 15.2%, and 8.6%, respectively. CONCLUSION PBPK modeling is commonly and widely used to research small-molecule targeted anti-cancer drugs.
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Affiliation(s)
- Xiaowen Wang
- Department of Pharmacy, Minhang Hospital, Fudan University, 170 Xinsong Road, Shanghai, China
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai, China
| | - Fang Chen
- Department of Pharmacy, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Nan Guo
- Department of Pharmacy, Minhang Hospital, Fudan University, 170 Xinsong Road, Shanghai, China
| | - Zhichun Gu
- Department of Pharmacy, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Houwen Lin
- Department of Pharmacy, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaoqiang Xiang
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai, China
| | - Yufei Shi
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai, China.
| | - Bing Han
- Department of Pharmacy, Minhang Hospital, Fudan University, 170 Xinsong Road, Shanghai, China.
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4
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Abla N, Howgate E, Rowland‐Yeo K, Dickins M, Bergagnini‐Kolev MC, Chen K, McFeely S, Bonner JJ, Santos LGA, Gobeau N, Burt H, Barter Z, Jones HM, Wesche D, Charman SA, Möhrle JJ, Burrows JN, Almond LM. Development and application of a PBPK modeling strategy to support antimalarial drug development. CPT Pharmacometrics Syst Pharmacol 2023; 12:1335-1346. [PMID: 37587640 PMCID: PMC10508484 DOI: 10.1002/psp4.13013] [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: 02/27/2023] [Revised: 05/26/2023] [Accepted: 06/28/2023] [Indexed: 08/18/2023] Open
Abstract
As part of a collaboration between Medicines for Malaria Venture (MMV), Certara UK and Monash University, physiologically-based pharmacokinetic (PBPK) models were developed for 20 antimalarials, using data obtained from standardized in vitro assays and clinical studies within the literature. The models have been applied within antimalarial drug development at MMV for more than 5 years. During this time, a strategy for their impactful use has evolved. All models are described in the supplementary material and are available to researchers. Case studies are also presented, demonstrating real-world development and clinical applications, including the assessment of the drug-drug interaction liability between combination partners or with co-administered drugs. This work emphasizes the benefit of PBPK modeling for antimalarial drug development and decision making, and presents a strategy to integrate it into the research and development process. It also provides a repository of shared information to benefit the global health research community.
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Affiliation(s)
- Nada Abla
- Medicines for Malaria VentureGenevaSwitzerland
| | | | | | | | | | | | | | | | | | | | | | - Zoe Barter
- Certara UK Ltd, Simcyp DivisionSheffieldUK
| | | | - David Wesche
- Certara USA, Integrated Drug DevelopmentGrand RapidsMichiganUSA
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5
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Ellis C, Inaba K, Van de Vuurst C, Ghrayeb A, Cory TJ. Drug-drug interactions between COVID-19 therapeutics and antiretroviral treatment: the evidence to date. Expert Opin Drug Metab Toxicol 2023; 19:795-806. [PMID: 37800561 PMCID: PMC10841549 DOI: 10.1080/17425255.2023.2267970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 10/04/2023] [Indexed: 10/07/2023]
Abstract
INTRODUCTION With new effective treatments for SARS-CoV-2, patient outcomes have greatly improved. However, new medications bring a risk of drug interactions with other medications. People living with HIV (PLWH) are at particular risk for these interactions due to heightened risk of immunosuppression, polypharmacy, and overlap in affected organs. It is critical to identify drug interactions are a significant barrier to care for PLWH. Establishing a better understanding of the pharmacologic relationships between COVID-19 therapies and antiretrovirals will improve patient-centered care in COVID-19. AREAS COVERED Potential drug-drug interactions between Human Immunodeficiency Virus (HIV) and COVID-19 treatments are detailed and reviewed here. The mechanisms seen in these interactions include alterations in metabolic enzymes, drug transporters, pharmacoenhancement, and organ toxicities. We also review the limitations and solutions that can be used to combat drug-drug interactions between these two disease states. EXPERT OPINION While current drug interactions are relatively mild between HIV and COVID-19 therapies, improvements in identifying these beforehand must take place as new therapies are approved. Antiretroviral therapy (ART) is essential in PLWH and must be maintained when treating COVID-19. As advancements in care occur, there is the possibility that newly approved drugs may have additional unknown interactions.
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Affiliation(s)
- Camden Ellis
- Department of Clinical Pharmacy and Translational Science, University of Tennessee Health Science Center College of Pharmacy, Memphis, USA
| | - Keita Inaba
- Department of Clinical Pharmacy and Translational Science, University of Tennessee Health Science Center College of Pharmacy, Memphis, USA
| | - Christine Van de Vuurst
- Department of Clinical Pharmacy and Translational Science, University of Tennessee Health Science Center College of Pharmacy, Memphis, USA
| | - Atheel Ghrayeb
- Department of Clinical Pharmacy and Translational Science, University of Tennessee Health Science Center College of Pharmacy, Memphis, USA
| | - Theodore James Cory
- Department of Clinical Pharmacy and Translational Science, University of Tennessee Health Science Center College of Pharmacy, Memphis, USA
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6
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Miners JO, Polasek TM, Hulin JA, Rowland A, Meech R. Drug-drug interactions that alter the exposure of glucuronidated drugs: Scope, UDP-glucuronosyltransferase (UGT) enzyme selectivity, mechanisms (inhibition and induction), and clinical significance. Pharmacol Ther 2023:108459. [PMID: 37263383 DOI: 10.1016/j.pharmthera.2023.108459] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 05/18/2023] [Accepted: 05/22/2023] [Indexed: 06/03/2023]
Abstract
Drug-drug interactions (DDIs) arising from the perturbation of drug metabolising enzyme activities represent both a clinical problem and a potential economic loss for the pharmaceutical industry. DDIs involving glucuronidated drugs have historically attracted little attention and there is a perception that interactions are of minor clinical relevance. This review critically examines the scope and aetiology of DDIs that result in altered exposure of glucuronidated drugs. Interaction mechanisms, namely inhibition and induction of UDP-glucuronosyltransferase (UGT) enzymes and the potential interplay with drug transporters, are reviewed in detail, as is the clinical significance of known DDIs. Altered victim drug exposure arising from modulation of UGT enzyme activities is relatively common and, notably, the incidence and importance of UGT induction as a DDI mechanism is greater than generally believed. Numerous DDIs are clinically relevant, resulting in either loss of efficacy or an increased risk of adverse effects, necessitating dose individualisation. Several generalisations relating to the likelihood of DDIs can be drawn from the known substrate and inhibitor selectivities of UGT enzymes, highlighting the importance of comprehensive reaction phenotyping studies at an early stage of drug development. Further, rigorous assessment of the DDI liability of new chemical entities that undergo glucuronidation to a significant extent has been recommended recently by regulatory guidance. Although evidence-based approaches exist for the in vitro characterisation of UGT enzyme inhibition and induction, the availability of drugs considered appropriate for use as 'probe' substrates in clinical DDI studies is limited and this should be research priority.
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Affiliation(s)
- John O Miners
- Discipline of Clinical Pharmacology and Flinders Centre for Innovation in Cancer, Flinders University College of Medicine and Public Health, Flinders University, Adelaide, Australia.
| | - Thomas M Polasek
- Certara, Princeton, NJ, USA; Centre for Medicines Use and Safety, Monash University, Melbourne, Australia
| | - Julie-Ann Hulin
- Discipline of Clinical Pharmacology and Flinders Centre for Innovation in Cancer, Flinders University College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | - Andrew Rowland
- Discipline of Clinical Pharmacology and Flinders Centre for Innovation in Cancer, Flinders University College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | - Robyn Meech
- Discipline of Clinical Pharmacology and Flinders Centre for Innovation in Cancer, Flinders University College of Medicine and Public Health, Flinders University, Adelaide, Australia
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7
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Sayyed SK, Quraishi M, Jobby R, Rameshkumar N, Kayalvizhi N, Krishnan M, Sonawane T. A computational overview of integrase strand transfer inhibitors (INSTIs) against emerging and evolving drug-resistant HIV-1 integrase mutants. Arch Microbiol 2023; 205:142. [PMID: 36966200 PMCID: PMC10039815 DOI: 10.1007/s00203-023-03461-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/26/2023] [Accepted: 03/01/2023] [Indexed: 03/27/2023]
Abstract
AIDS (Acquired immunodeficiency syndrome) is one of the chronic and potentially life-threatening epidemics across the world. Hitherto, the non-existence of definitive drugs that could completely cure the Human immunodeficiency virus (HIV) implies an urgent necessity for the discovery of novel anti-HIV agents. Since integration is the most crucial stage in retroviral replication, hindering it can inhibit overall viral transmission. The 5 FDA-approved integrase inhibitors were computationally investigated, especially owing to the rising multiple mutations against their susceptibility. This comparative study will open new possibilities to guide the rational design of novel lead compounds for antiretroviral therapies (ARTs), more specifically the structure-based design of novel Integrase strand transfer inhibitors (INSTIs) that may possess a better resistance profile than present drugs. Further, we have discussed potent anti-HIV natural compounds and their interactions as an alternative approach, recommending the urgent need to tap into the rich vein of indigenous knowledge for reverse pharmacology. Moreover, herein, we discuss existing evidence that might change in the near future.
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Affiliation(s)
- Sharif Karim Sayyed
- Amity Institute of Biotechnology, Amity University, Mumbai, Maharashtra, 410206, India
| | - Marzuqa Quraishi
- Amity Institute of Biotechnology, Amity University, Mumbai, Maharashtra, 410206, India
| | - Renitta Jobby
- Amity Institute of Biotechnology, Amity University, Mumbai, Maharashtra, 410206, India
| | | | - Nagarajan Kayalvizhi
- Regenerative Medicine Laboratory, Department of Zoology, School of Life Sciences, Periyar University, Salem, Tamil Nadu, 636011, India
| | | | - Tareeka Sonawane
- Amity Institute of Biotechnology, Amity University, Mumbai, Maharashtra, 410206, India.
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8
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Hu W, Zhang W, Zhou Y, Luo Y, Sun X, Xu H, Shi S, Li T, Xu Y, Yang Q, Qiu Y, Zhu F, Dai H. MecDDI: Clarified Drug-Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug-Drug Interaction Prediction. J Chem Inf Model 2023; 63:1626-1636. [PMID: 36802582 DOI: 10.1021/acs.jcim.2c01656] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Drug-drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled "MecDDI" was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: https://idrblab.org/mecddi/.
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Affiliation(s)
- Wei Hu
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Ying Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Huimin Xu
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Shuiyang Shi
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Teng Li
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Yichao Xu
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Qianqian Yang
- Department of Pharmacy, Affiliated Hangzhou First Peoples Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China.,Clinical Pharmacy Research Center, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Feng Zhu
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Haibin Dai
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.,Clinical Pharmacy Research Center, Zhejiang University School of Medicine, Hangzhou 310009, China
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9
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Girardin F, Manuel O, Marzolini C, Buclin T. Evaluating the risk of drug-drug interactions with pharmacokinetic boosters: the case of ritonavir-enhanced nirmatrelvir to prevent severe COVID-19. Clin Microbiol Infect 2022; 28:1044-1046. [PMID: 35358684 PMCID: PMC8958820 DOI: 10.1016/j.cmi.2022.03.030] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/04/2022] [Accepted: 03/07/2022] [Indexed: 01/14/2023]
Affiliation(s)
- François Girardin
- Division of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital, Faculty of Medicine, University of Lausanne, Lausanne, Switzerland; Division of Clinical Pharmacology and Toxicology, Department of Anesthesiology, Clinical Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland.
| | - Oriol Manuel
- Infectious Diseases Service and Transplantation Centre, Lausanne, Lausanne University Hospital, Faculty of Medicine, University of Lausanne, Lausanne, Switzerland
| | - Catia Marzolini
- Division of Infectious Diseases and Hospital Epidemiology, Departments of Medicine and Clinical Research, University Hospital of Basel and University of Basel, Basel, Switzerland; Department of Molecular and Clinical Pharmacology, University of Liverpool, United Kingdom
| | - Thierry Buclin
- Division of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital, Faculty of Medicine, University of Lausanne, Lausanne, Switzerland
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10
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Talkington AM, Wessler T, Lai SK, Cao Y, Forest MG. Experimental Data and PBPK Modeling Quantify Antibody Interference in PEGylated Drug Carrier Delivery. Bull Math Biol 2021; 83:123. [PMID: 34751832 PMCID: PMC8576315 DOI: 10.1007/s11538-021-00950-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 09/27/2021] [Indexed: 12/30/2022]
Abstract
Physiologically-based pharmacokinetic (PBPK) modeling is a popular drug development tool that integrates physiology, drug physicochemical properties, preclinical data, and clinical information to predict drug systemic disposition. Since PBPK models seek to capture complex physiology, parameter uncertainty and variability is a prevailing challenge: there are often more compartments (e.g., organs, each with drug flux and retention mechanisms, and associated model parameters) than can be simultaneously measured. To improve the fidelity of PBPK modeling, one approach is to search and optimize within the high-dimensional model parameter space, based on experimental time-series measurements of drug distributions. Here, we employ Latin Hypercube Sampling (LHS) on a PBPK model of PEG-liposomes (PL) that tracks biodistribution in an 8-compartment mouse circulatory system, in the presence (APA+) or absence (naïve) of anti-PEG antibodies (APA). Near-continuous experimental measurements of PL concentration during the first hour post-injection from the liver, spleen, kidney, muscle, lung, and blood plasma, based on PET/CT imaging in live mice, are used as truth sets with LHS to infer optimal parameter ranges for the full PBPK model. The data and model quantify that PL retention in the liver is the primary differentiator of biodistribution patterns in naïve versus APA+ mice, and spleen the secondary differentiator. Retention of PEGylated nanomedicines is substantially amplified in APA+ mice, likely due to PL-bound APA engaging specific receptors in the liver and spleen that bind antibody Fc domains. Our work illustrates how applying LHS to PBPK models can further mechanistic understanding of the biodistribution and antibody-mediated clearance of specific drugs.
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Affiliation(s)
- Anne M Talkington
- Program in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA.
| | - Timothy Wessler
- Department of Mathematics, University of North Carolina, Chapel Hill, NC, USA
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Samuel K Lai
- Program in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA
- Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
- UNC/NCSU Joint Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC, USA
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Yanguang Cao
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina, Chapel Hill, NC, USA
| | - M Gregory Forest
- Program in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA.
- Department of Mathematics, University of North Carolina, Chapel Hill, NC, USA.
- UNC/NCSU Joint Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC, USA.
- Department of Applied Physical Sciences, University of North Carolina, Chapel Hill, NC, USA.
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11
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Venkatakrishnan K, van der Graaf PH. Model-Informed Drug Development: Connecting the Dots With a Totality of Evidence Mindset to Advance Therapeutics. Clin Pharmacol Ther 2021; 110:1147-1154. [PMID: 34658027 DOI: 10.1002/cpt.2422] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 09/09/2021] [Indexed: 01/01/2023]
Affiliation(s)
- Karthik Venkatakrishnan
- EMD Serono Research & Development Inc., Billerica, MA, USA.,A Business of Merck KGaA, Darmstadt, Germany
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12
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Stader F, Battegay M, Sendi P, Marzolini C. Physiologically Based Pharmacokinetic Modelling to Investigate the Impact of the Cytokine Storm on CYP3A Drug Pharmacokinetics in COVID-19 Patients. Clin Pharmacol Ther 2021; 111:579-584. [PMID: 34496043 PMCID: PMC8652944 DOI: 10.1002/cpt.2402] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 08/03/2021] [Indexed: 12/16/2022]
Abstract
Patients with coronavirus disease 2019 (COVID‐19) may experience a cytokine storm with elevated interleukin‐6 (IL‐6) levels in response to severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2). IL‐6 suppresses hepatic enzymes, including CYP3A; however, the effect on drug exposure and drug‐drug interaction magnitudes of the cytokine storm and resulting elevated IL‐6 levels have not been characterized in patients with COVID‐19. We used physiologically‐based pharmacokinetic (PBPK) modeling to simulate the effect of inflammation on the pharmacokinetics of CYP3A metabolized drugs. A PBPK model was developed for lopinavir boosted with ritonavir (LPV/r), using clinically observed data from people living with HIV (PLWH). The inhibition of CYPs by IL‐6 was implemented by a semimechanistic suppression model and verified against clinical data from patients with COVID‐19, treated with LPV/r. Subsequently, the verified model was used to simulate the effect of various clinically observed IL‐6 levels on the exposure of LPV/r and midazolam, a CYP3A model drug. Clinically observed LPV/r concentrations in PLWH and patients with COVID‐19 were predicted within the 95% confidence interval of the simulation results, demonstrating its predictive capability. Simulations indicated a twofold higher LPV exposure in patients with COVID‐19 compared with PLWH, whereas ritonavir exposure was predicted to be comparable. Varying IL‐6 levels under COVID‐19 had only a marginal effect on LPV/r pharmacokinetics according to our model. Simulations showed that a cytokine storm increased the exposure of the CYP3A paradigm substrate midazolam by 40%. Our simulations suggest that CYP3A metabolism is altered in patients with COVID‐19 having increased cytokine release. Caution is required when prescribing narrow therapeutic index drugs particularly in the presence of strong CYP3A inhibitors.
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Affiliation(s)
- Felix Stader
- Departments of Medicine and Clinical Research, University Hospital Basel, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Manuel Battegay
- Departments of Medicine and Clinical Research, University Hospital Basel, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Parham Sendi
- Departments of Medicine and Clinical Research, University Hospital Basel, Basel, Switzerland.,University of Basel, Basel, Switzerland.,Institute for Infectious Diseases, University of Bern, Bern, Switzerland
| | - Catia Marzolini
- Departments of Medicine and Clinical Research, University Hospital Basel, Basel, Switzerland.,University of Basel, Basel, Switzerland.,Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, UK
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