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Russell LE, Yadav J, Maldonato BJ, Chien HC, Zou L, Vergara AG, Villavicencio EG. Transporter-mediated drug-drug interactions: regulatory guidelines, in vitro and in vivo methodologies and translation, special populations, and the blood-brain barrier. Drug Metab Rev 2024:1-28. [PMID: 38967415 DOI: 10.1080/03602532.2024.2364591] [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: 02/13/2024] [Accepted: 05/31/2024] [Indexed: 07/06/2024]
Abstract
This review, part of a special issue on drug-drug interactions (DDIs) spearheaded by the International Society for the Study of Xenobiotics (ISSX) New Investigators, explores the critical role of drug transporters in absorption, disposition, and clearance in the context of DDIs. Over the past two decades, significant advances have been made in understanding the clinical relevance of these transporters. Current knowledge on key uptake and efflux transporters that affect drug disposition and development is summarized. Regulatory guidelines from the FDA, EMA, and PMDA that inform the evaluation of potential transporter-mediated DDIs are discussed in detail. Methodologies for preclinical and clinical testing to assess potential DDIs are reviewed, with an emphasis on the utility of physiologically based pharmacokinetic (PBPK) modeling. This includes the application of relative abundance and expression factors to predict human pharmacokinetics (PK) using preclinical data, integrating the latest regulatory guidelines. Considerations for assessing transporter-mediated DDIs in special populations, including pediatric, hepatic, and renal impairment groups, are provided. Additionally, the impact of transporters at the blood-brain barrier (BBB) on the disposition of CNS-related drugs is explored. Enhancing the understanding of drug transporters and their role in drug disposition and toxicity can improve efficacy and reduce adverse effects. Continued research is essential to bridge remaining gaps in knowledge, particularly in comparison with cytochrome P450 (CYP) enzymes.
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Affiliation(s)
- Laura E Russell
- Department of Quantitative, Translational, and ADME Sciences, AbbVie Inc, North Chicago, IL, USA
| | - Jaydeep Yadav
- Department of Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics, Merck & Co., Inc, Boston, MA, USA
| | - Benjamin J Maldonato
- Department of Nonclinical Development and Clinical Pharmacology, Revolution Medicines, Inc, Redwood City, CA, USA
| | - Huan-Chieh Chien
- Department of Pharmacokinetics and Drug Metabolism, Amgen Inc, South San Francisco, CA, USA
| | - Ling Zou
- Department of Pharmacokinetics and Drug Metabolism, Amgen Inc, South San Francisco, CA, USA
| | - Ana G Vergara
- Department of Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics, Merck & Co., Inc, Rahway, NJ, USA
| | - Erick G Villavicencio
- Department of Biology-Discovery, Imaging and Functional Genomics, Merck & Co., Inc, Rahway, NJ, USA
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Preiss LC, Georgi K, Lauschke VM, Petersson C. Comparison of Human Long-Term Liver Models for Clearance Prediction of Slowly Metabolized Compounds. Drug Metab Dispos 2024; 52:539-547. [PMID: 38604730 DOI: 10.1124/dmd.123.001638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/06/2024] [Accepted: 03/19/2024] [Indexed: 04/13/2024] Open
Abstract
The accurate prediction of human clearance is an important task during drug development. The proportion of low clearance compounds has increased in drug development pipelines across the industry since such compounds may be dosed in lower amounts and at lower frequency. These type of compounds present new challenges to in vitro systems used for clearance extrapolation. In this study, we compared the accuracy of clearance predictions of suspension culture to four different long-term stable in vitro liver models, including HepaRG sandwich culture, the Hµrel stochastic co-culture, the Hepatopac micropatterned co-culture (MPCC), and a micro-array spheroid culture. Hepatocytes in long-term stable systems remained viable and active over several days of incubation. Although intrinsic clearance values were generally high in suspension culture, clearance of low turnover compounds could frequently not be determined using this method. Metabolic activity and intrinsic clearance values from HepaRG cultures were low and, consequently, many compounds with low turnover did not show significant decline despite long incubation times. Similarly, stochastic co-cultures occasionally failed to show significant turnover for multiple low and medium turnover compounds. Among the different methods, MPCCs and spheroids provided the most consistent measurements. Notably, all culture methods resulted in underprediction of clearance; this could, however, be compensated for by regression correction. Combined, the results indicate that spheroid culture as well as the MPCC system provide adequate in vitro tools for human extrapolation for compounds with low metabolic turnover. SIGNIFICANCE STATEMENT: In this study, we compared suspension cultures, HepaRG sandwich cultures, the Hµrel liver stochastic co-cultures, the Hepatopac micropatterned co-cultures (MPCC), and micro-array spheroid cultures for low clearance determination and prediction. Overall, HepaRG and suspension cultures showed modest value for the low determination and prediction of clearance compounds. The micro-array spheroid culture resulted in the most robust clearance measurements, whereas using the MPCC resulted in the most accurate prediction for low clearance compounds.
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Affiliation(s)
- Lena C Preiss
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (L.C.P., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), The Healthcare Business of Merck KGaA, Darmstadt, Germany (L.C.P., K.G., C.P.); Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.); and University of Tuebingen, Tuebingen, Germany (V.M.L.)
| | - Katrin Georgi
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (L.C.P., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), The Healthcare Business of Merck KGaA, Darmstadt, Germany (L.C.P., K.G., C.P.); Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.); and University of Tuebingen, Tuebingen, Germany (V.M.L.)
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (L.C.P., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), The Healthcare Business of Merck KGaA, Darmstadt, Germany (L.C.P., K.G., C.P.); Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.); and University of Tuebingen, Tuebingen, Germany (V.M.L.)
| | - Carl Petersson
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (L.C.P., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), The Healthcare Business of Merck KGaA, Darmstadt, Germany (L.C.P., K.G., C.P.); Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.); and University of Tuebingen, Tuebingen, Germany (V.M.L.)
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3
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Gabor-Worwa E, Kowal-Chwast A, Gaud N, Gogola D, Littlewood P, Smoluch M, Brzózka K, Kus K. Uridine 5'-Diphospho-glucuronosyltransferase 1A3 (UGT1A3) Prediction of Hepatic Clearance of Organic Anion Transporting Polypeptide 1B3 (OATP1B3) Substrate Telmisartan by Glucuronidation Using In Vitro-In Vivo Extrapolation (IVIVE). Eur J Drug Metab Pharmacokinet 2024; 49:393-403. [PMID: 38642299 DOI: 10.1007/s13318-024-00895-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/27/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND AND OBJECTIVE The prediction of pharmacokinetic parameters for drugs metabolised by cytochrome P450 enzymes has been the subject of active research for many years, while the application of in vitro-in vivo extrapolation (IVIVE) techniques for non-cytochrome P450 enzymes has not been thoroughly evaluated. There is still no established quantitative method for predicting hepatic clearance of drugs metabolised by uridine 5'-diphospho-glucuronosyltransferases (UGTs), not to mention those which undergo hepatic uptake. The objective of the study was to predict the human hepatic clearance for telmisartan based on in vitro metabolic stability and hepatic uptake results. METHODS Telmisartan was examined in liver systems, allowing to estimate intrinsic clearance (CLint, in vitro) based on the substrate disappearance rate with the use of liquid chromatography tandem mass spectrometry (LC-MS/MS) technique. Obtained CLint, in vitro values were corrected for corresponding unbound fractions. Prediction of human hepatic clearance was made from scaled unbound CLint, in vitro data with the use of the well-stirred model, and finally referenced to the literature value of observed clearance in humans, allowing determination of the essential scaling factors. RESULTS The in vitro scaled CLint, in vitro by UGT1A3 was assessed using three systems, human hepatocytes, liver microsomes, and recombinant enzymes. Obtained values were scaled and hepatic metabolism clearance was predicted, resulting in significant clearance underprediction. Utilization of the extended clearance concept (ECC) and hepatic uptake improved prediction of hepatic metabolism clearance. The scaling factors for hepatocytes, assessing the in vitro-in vivo difference, changed from sixfold difference to only twofold difference with the application of the ECC. CONCLUSIONS The study showed that taking into consideration hepatic uptake of a drug allows us to obtain satisfactory scaling factors, hence enabling the prediction of in vivo hepatic glucuronidation from in vitro data.
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Affiliation(s)
- Ewelina Gabor-Worwa
- Department of Drug Metabolism and Pharmacokinetics, Ryvu Therapeutics S.A., Sternbacha 2 Street, 30-394, Krakow, Poland.
- Department of Analytical Chemistry and Biochemistry, Faculty of Materials Science and Ceramics, AGH University of Krakow, Mickiewicza 30 Street, 30-059, Krakow, Poland.
| | - Anna Kowal-Chwast
- Department of Drug Metabolism and Pharmacokinetics, Ryvu Therapeutics S.A., Sternbacha 2 Street, 30-394, Krakow, Poland
- Department of Analytical Chemistry and Biochemistry, Faculty of Materials Science and Ceramics, AGH University of Krakow, Mickiewicza 30 Street, 30-059, Krakow, Poland
| | - Nilesh Gaud
- Department of Drug Metabolism and Pharmacokinetics, Ryvu Therapeutics S.A., Sternbacha 2 Street, 30-394, Krakow, Poland
| | - Dawid Gogola
- Department of Drug Metabolism and Pharmacokinetics, Ryvu Therapeutics S.A., Sternbacha 2 Street, 30-394, Krakow, Poland
| | - Peter Littlewood
- Department of Drug Metabolism and Pharmacokinetics, Ryvu Therapeutics S.A., Sternbacha 2 Street, 30-394, Krakow, Poland
| | - Marek Smoluch
- Department of Analytical Chemistry and Biochemistry, Faculty of Materials Science and Ceramics, AGH University of Krakow, Mickiewicza 30 Street, 30-059, Krakow, Poland
| | - Krzysztof Brzózka
- Department of Drug Metabolism and Pharmacokinetics, Ryvu Therapeutics S.A., Sternbacha 2 Street, 30-394, Krakow, Poland
| | - Kamil Kus
- Department of Drug Metabolism and Pharmacokinetics, Ryvu Therapeutics S.A., Sternbacha 2 Street, 30-394, Krakow, Poland
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Francis L, Ogungbenro K, De Bruyn T, Houston JB, Hallifax D. Exploring the Boundaries for In Vitro-In Vivo Extrapolation: Use of Isolated Rat Hepatocytes in Co-culture and Impact of Albumin Binding Properties in the Prediction of Clearance of Various Drug Types. Drug Metab Dispos 2023; 51:1463-1473. [PMID: 37580106 DOI: 10.1124/dmd.123.001309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 07/15/2023] [Accepted: 08/08/2023] [Indexed: 08/16/2023] Open
Abstract
Prediction of hepatic clearance of drugs (via uptake or metabolism) from in vitro systems continues to be problematic, particularly when plasma protein binding is high. The following work explores simultaneous assessment of both clearance processes, focusing on a commercial hepatocyte-fibroblast co-culture system (HμREL) over a 24-hour period using six probe drugs (ranging in metabolic and transporter clearance and low-to-high plasma protein binding). A rat hepatocyte co-culture assay was established using drug depletion (measuring both medium and total concentrations) and cell uptake kinetic analysis, both in the presence and absence of plasma protein (1% bovine serum albumin). Secretion of endogenous albumin was monitored as a marker of viability, and this reached 0.004% in incubations (at a rate similar to in vivo synthesis). Binding to stromal cells was substantial and required appropriate correction factors. Drug concentration-time courses were analyzed both by conventional methods and a mechanistic cell model prior to in vivo extrapolation. Clearance assayed by drug depletion in conventional suspended rat hepatocytes provided a benchmark to evaluate co-culture value. Addition of albumin appeared to improve predictions for some compounds (where fraction unbound in the medium is less than 0.1); however, for high-binding drugs, albumin significantly limited quantification and thus predictions. Overall, these results highlight ongoing challenges concerning in vitro hepatocyte system complexity and limitations of practical expediency. Considering this, more reliable measurement of hepatically cleared compounds seems possible through judicious use of available hepatocyte systems, including co-culture systems, as described herein; this would include those compounds with low metabolic turnover but high active uptake clearance. SIGNIFICANCE STATEMENT: Co-culture systems offer a more advanced tool than standard hepatocytes, with the ability to be cultured for longer periods of time, yet their potential as an in vitro tool has not been extensively assessed. We evaluate the strengths and limitations of the HμREL system using six drugs representing various metabolic and transporter-mediated clearance pathways with various degrees of albumin binding. Studies in the presence/absence of albumin allow in vitro-in vivo extrapolation and a framework to maximize their utility.
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Affiliation(s)
- Laura Francis
- 1Centre of Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom (L.F., K.O., J.B.H., D.H.) and Genentech, Inc., South San Francisco, California (T.D.B.)
| | - Kayode Ogungbenro
- 1Centre of Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom (L.F., K.O., J.B.H., D.H.) and Genentech, Inc., South San Francisco, California (T.D.B.)
| | - Tom De Bruyn
- 1Centre of Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom (L.F., K.O., J.B.H., D.H.) and Genentech, Inc., South San Francisco, California (T.D.B.)
| | - J Brian Houston
- 1Centre of Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom (L.F., K.O., J.B.H., D.H.) and Genentech, Inc., South San Francisco, California (T.D.B.)
| | - David Hallifax
- 1Centre of Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom (L.F., K.O., J.B.H., D.H.) and Genentech, Inc., South San Francisco, California (T.D.B.)
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Andrews-Morger A, Reutlinger M, Parrott N, Olivares-Morales A. A Machine Learning Framework to Improve Rat Clearance Predictions and Inform Physiologically Based Pharmacokinetic Modeling. Mol Pharm 2023; 20:5052-5065. [PMID: 37713584 DOI: 10.1021/acs.molpharmaceut.3c00374] [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] [Indexed: 09/17/2023]
Abstract
During drug discovery and development, achieving appropriate pharmacokinetics is key to establishment of the efficacy and safety of new drugs. Physiologically based pharmacokinetic (PBPK) models integrating in vitro-to-in vivo extrapolation have become an essential in silico tool to achieve this goal. In this context, the most important and probably most challenging pharmacokinetic parameter to estimate is the clearance. Recent work on high-throughput PBPK modeling during drug discovery has shown that a good estimate of the unbound intrinsic clearance (CLint,u,) is the key factor for useful PBPK application. In this work, three different machine learning-based strategies were explored to predict the rat CLint,u as the input into PBPK. Therefore, in vivo and in vitro data was collected for a total of 2639 proprietary compounds. The strategies were compared to the standard in vitro bottom-up approach. Using the well-stirred liver model to back-calculate in vivo CLint,u from in vivo rat clearance and then training a machine learning model on this CLint,u led to more accurate clearance predictions (absolute average fold error (AAFE) 3.1 in temporal cross-validation) than the bottom-up approach (AAFE 3.6-16, depending on the scaling method) and has the advantage that no experimental in vitro data is needed. However, building a machine learning model on the bias between the back-calculated in vivo CLint,u and the bottom-up scaled in vitro CLint,u also performed well. For example, using unbound hepatocyte scaling, adding the bias prediction improved the AAFE in the temporal cross-validation from 16 for bottom-up to 2.9 together with the bias prediction. Similarly, the log Pearson r2 improved from 0.1 to 0.29. Although it would still require in vitro measurement of CLint,u., using unbound scaling for the bottom-up approach, the need for correction of the fu,inc by fu,p data is circumvented. While the above-described ML models were built on all data points available per approach, it is discussed that evaluation comparison across all approaches could only be performed on a subset because ca. 75% of the molecules had missing or unquantifiable measurements of the fraction unbound in plasma or in vitro unbound intrinsic clearance, or they dropped out due to the blood-flow limitation assumed by the well-stirred model. Advantageously, by predicting CLint,u as the input into PBPK, existing workflows can be reused and the prediction of the in vivo clearance and other PK parameters can be improved.
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Affiliation(s)
- Andrea Andrews-Morger
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Michael Reutlinger
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Neil Parrott
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Andrés Olivares-Morales
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
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Zhang Y, Umehara K, Romeo AA, Singh N, Cantrill C, Savage M, Chen E, Zhang W, Parrot NJ, Paehler A. Evaluation of the drug disposition of RO7049389 with in vitro data and human mass balance supported by physiologically based pharmacokinetic modelling. Br J Clin Pharmacol 2023; 89:3079-3091. [PMID: 37264516 DOI: 10.1111/bcp.15809] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/21/2023] [Accepted: 05/24/2023] [Indexed: 06/03/2023] Open
Abstract
AIMS RO7049389 (linvencorvir) is a developmental oral treatment for chronic hepatitis B virus infection. The aim of this work was to conduct mass balance (MB) and absolute bioavailability (BA) analyses in healthy volunteers, alongside in vitro evaluations of the metabolism of RO7049389 and a major circulating active metabolite M5 in human hepatocytes, and physiologically based pharmacokinetic (PBPK) modelling to refine the underlying drug disposition paradigm. METHODS Participants in the clinical study (MB: Caucasian, male, n = 6; BA: Caucasian and Asian, male and female, n = 16, 8 in each ethnic groups) received oral [14 C] or unlabelled RO7049389 (600/1000 mg) followed by 100 μg intravenous [13 C]RO7049389. Metabolic pathways with fractions metabolized-obtained from the in vitro incubation results of 10 μM [14 C]RO7049389 and 1 μM M5 with (long-term cocultured) human hepatocytes in the absence and presence of the cytochrome P450 3A4 (CYP3A4) inhibitor itraconazole-were used to complement the PBPK models, alongside the clinical MB and BA data. RESULTS The model performance in predicting the pharmacokinetic profiles of RO7049389 and M5 aligned with clinical observations in Caucasians and was also successfully applied to Asians. Accordingly, the drug disposition pathways for RO7049389 were postulated with newly characterized estimates of the fractions: biliary excretion by P-glycoprotein (~41%), direct glucuronidation via uridine 5'-diphosphoglucuronosyltransferase 1A3 (~11%), hexose conjugation (~6%), oxidation by CYP3A4 (~28%) and other oxidation reactions (~9%). CONCLUSION These results support the ongoing clinical development program for RO7049389 and highlight the broader value of PBPK and MB analyses in drug development.
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Affiliation(s)
- Yuchen Zhang
- Roche Pharma Research & Early Development, China Innovation Center of Roche, Shanghai, China
| | - Kenichi Umehara
- Roche Pharma Research & Early Development, Roche Innovation Center, Basel, Switzerland
| | - Andrea A Romeo
- Roche Pharma Research & Early Development, Roche Innovation Center, Basel, Switzerland
| | | | - Carina Cantrill
- Roche Pharma Research & Early Development, Roche Innovation Center, Basel, Switzerland
| | | | | | - Wen Zhang
- Roche Pharma Research & Early Development, China Innovation Center of Roche, Shanghai, China
| | - Neil John Parrot
- Roche Pharma Research & Early Development, Roche Innovation Center, Basel, Switzerland
| | - Axel Paehler
- Roche Pharma Research & Early Development, Roche Innovation Center, Basel, Switzerland
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Manevski N, Umehara K, Parrott N. Drug Design and Success of Prospective Mouse In Vitro-In Vivo Extrapolation (IVIVE) for Predictions of Plasma Clearance (CL p) from Hepatocyte Intrinsic Clearance (CL int). Mol Pharm 2023. [PMID: 37235687 DOI: 10.1021/acs.molpharmaceut.2c01001] [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: 05/28/2023]
Abstract
Hepatocyte intrinsic clearance (CLint) and methods of in vitro-in vivo extrapolation (IVIVE) are often used to predict plasma clearance (CLp) in drug discovery. While the prediction success of this approach is dependent on the chemotype, specific molecular properties and drug design features that govern these outcomes are poorly understood. To address this challenge, we investigated the success of prospective mouse CLp IVIVE across 2142 chemically diverse compounds. Dilution scaling, which assumes that the free fraction in hepatocyte incubations (fu,inc) is governed by binding to the 10% of serum in the incubation medium, was used as our default CLp IVIVE approach. Results show that predictions of CLp are better for smaller (molecular weight (MW) < 500 Da), less polar (total polar surface area (TPSA) < 100 Å2, hydrogen bond donor (HBD) ≤1, hydrogen bond acceptor (HBA) ≤ 6), lipophilic (log D > 3), and neutral compounds, with low HBD count playing the key role. If compounds are classified according to their chemical space, predictions were good for compounds resembling central nervous system (CNS) drugs [average absolute fold error (AAFE) of 2.05, average fold error (AFE) of 0.90], moderate for classical druglike compounds (according to Lipinski, Veber, and Ghose guidelines; AAFE of 2.55; AFE of 0.68), and poor for nonclassical "beyond the rule of 5" compounds (AAFE of 3.31; AFE of 0.41). From the perspective of measured druglike properties, predictions of CLp were better for compounds with moderate-to-high hepatocyte CLint (>10 μL/min/106 cells), high passive cellular permeability (Papp > 100 nm/s), and moderate observed CLp (5-50 mL/min/kg). Influences of plasma protein binding (fu,p) and P-glycoprotein (Pgp) apical efflux ratio (AP-ER) were less pronounced. If the extended clearance classification system (ECCS) is applied, predictions were good for class 2 (Papp > 50 nm/s; neutral or basic; AAFE of 2.35; AFE of 0.70) and acceptable for class 1A compounds (AAFE of 2.98; AFE of 0.70). Classes 1B, 3 A/B, and 4 showed poor outcomes (AAFE > 3.80; AFE < 0.60). Functional groups trending toward weaker CLp IVIVE were esters, carbamates, sulfonamides, carboxylic acids, ketones, primary and secondary amines, primary alcohols, oxetanes, and compounds liable to aldehyde oxidase metabolism, likely due to multifactorial reasons. Multivariate analysis showed that multiple properties are relevant, combining together to define the overall success of CLp IVIVE. Our results indicate that the current practice of prospective CLp IVIVE is suitable only for CNS-like compounds and well-behaved classical druglike space (e.g., high permeability or ECCS class 2) without challenging functional groups. Unfortunately, based on existing mouse data, prospective CLp IVIVE for complex and nonclassical chemotypes is poor and hardly better than random guessing. This is likely due to complexities such as extrahepatic metabolism and transporter-mediated disposition which are poorly captured by this methodology. With small-molecule drug discovery increasingly evolving toward nonclassical and complex chemotypes, existing CLp IVIVE methodology will require improvement. While empirical correction factors may bridge the gap in the near future, improved and new in vitro assays, data integration models, and machine learning (ML) methods are increasingly needed to address this challenge and reduce the number of nonclinical pharmacokinetic (PK) studies.
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Affiliation(s)
- Nenad Manevski
- Roche Pharmaceutical Research and Early Development (pRED), Roche Innovation Center Basel, 4070 Basel, Switzerland
| | - Kenichi Umehara
- Roche Pharmaceutical Research and Early Development (pRED), Roche Innovation Center Basel, 4070 Basel, Switzerland
| | - Neil Parrott
- Roche Pharmaceutical Research and Early Development (pRED), Roche Innovation Center Basel, 4070 Basel, Switzerland
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8
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Stoyanova R, Katzberger PM, Komissarov L, Khadhraoui A, Sach-Peltason L, Groebke Zbinden K, Schindler T, Manevski N. Computational Predictions of Nonclinical Pharmacokinetics at the Drug Design Stage. J Chem Inf Model 2023; 63:442-458. [PMID: 36595708 DOI: 10.1021/acs.jcim.2c01134] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Although computational predictions of pharmacokinetics (PK) are desirable at the drug design stage, existing approaches are often limited by prediction accuracy and human interpretability. Using a discovery data set of mouse and rat PK studies at Roche (9,685 unique compounds), we performed a proof-of-concept study to predict key PK properties from chemical structure alone, including plasma clearance (CLp), volume of distribution at steady-state (Vss), and oral bioavailability (F). Ten machine learning (ML) models were evaluated, including Single-Task, Multitask, and transfer learning approaches (i.e., pretraining with in vitro data). In addition to prediction accuracy, we emphasized human interpretability of outcomes, especially the quantification of uncertainty, applicability domains, and explanations of predictions in terms of molecular features. Results show that intravenous (IV) PK properties (CLp and Vss) can be predicted with good precision (average absolute fold error, AAFE of 1.96-2.84 depending on data split) and low bias (average fold error, AFE of 0.98-1.36), with AutoGluon, Gaussian Process Regressor (GP), and ChemProp displaying the best performance. Driven by higher complexity of oral PK studies, predictions of F were more challenging, with the best AAFE values of 2.35-2.60 and higher overprediction bias (AFE of 1.45-1.62). Multi-Task approaches and pretraining of ChemProp neural networks with in vitro data showed similar precision to Single-Task models but helped reduce the bias and increase correlations between observations and predictions. A combination of GP-computed prediction variance, molecular clustering, and dimensionality-reduction provided valuable quantitative insights into prediction uncertainty and applicability domains. SHAPley Additive exPlanations (SHAPs) highlighted molecular features contributing to prediction outcomes of Vss, providing explanations that could aid drug design. Combined results show that computational predictions of PK are feasible at the drug design stage, with several ML technologies converging to successfully leverage historical PK data sets. Further studies are needed to unlock the full potential of this approach, especially with respect to data set sizes and quality, transfer learning between in vitro and in vivo data sets, model-independent quantification of uncertainty, and explainability of predictions.
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Affiliation(s)
- Raya Stoyanova
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, 4070Basel, Switzerland
| | - Paul Maximilian Katzberger
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, 4070Basel, Switzerland
| | - Leonid Komissarov
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, 4070Basel, Switzerland
| | - Aous Khadhraoui
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, 4070Basel, Switzerland
| | - Lisa Sach-Peltason
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, 4070Basel, Switzerland
| | - Katrin Groebke Zbinden
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, 4070Basel, Switzerland
| | - Torsten Schindler
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, 4070Basel, Switzerland
| | - Nenad Manevski
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, 4070Basel, Switzerland
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9
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Parrott N, Manevski N, Olivares-Morales A. Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or In Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds. Mol Pharm 2022; 19:3858-3868. [PMID: 36150125 DOI: 10.1021/acs.molpharmaceut.2c00350] [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: 11/29/2022]
Abstract
While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are complex and often unfavorable. To predict clinical PK in early drug discovery, we built human physiologically based PK (PBPK) models integrating either (i) machine learning (ML)-predicted properties or (ii) discovery stage in vitro data. Our test set was composed of 12 challenging development compounds with high lipophilicity (mean calculated log P 4.2), low plasma-free fraction (50% of compounds with fu,p < 1%), and low aqueous solubility. Predictions focused on key human PK parameters, including plasma clearance (CL), volume of distribution at steady state (Vss), and oral bioavailability (%F). For predictions of CL, the ML inputs showed acceptable accuracy and slight underprediction bias [an average absolute fold error (AAFE) of 3.55; an average fold error (AFE) of 0.95]. Surprisingly, use of measured data only slightly improved accuracy but introduced an overprediction bias (AAFE = 3.35; AFE = 2.63). Predictions of Vss were more successful, with both ML (AAFE = 2.21; AFE = 0.90) and in vitro (AAFE = 2.24; AFE = 1.72) inputs showing good accuracy and moderate bias. The %F was poorly predicted using ML inputs [average absolute prediction error (AAPE) of 45%], and use of measured data for solubility and permeability improved this to 34%. Sensitivity analysis showed that predictions of CL limited the overall accuracy of human PK predictions, partly due to high nonspecific binding of lipophilic compounds, leading to uncertainty of unbound clearance. For accurate predictions of %F, solubility was the key factor. Despite current limitations, this work encourages further development of ML models and integration of their results within PBPK models to enable human PK prediction at the drug design stage, even before compounds are synthesized. Further evaluation of this approach with more diverse chemical types is warranted.
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Affiliation(s)
- Neil Parrott
- Pharmaceutical Sciences, Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070 Basel, Switzerland
| | - Nenad Manevski
- Pharmaceutical Sciences, Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070 Basel, Switzerland
| | - Andrés Olivares-Morales
- Pharmaceutical Sciences, Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070 Basel, Switzerland
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10
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Rynn C, Umehara K, Jiang T, Ait-Goughoulte M, Parrott N. A translational strategy employing physiologically based modeling to predict the pharmacological active dose of RO7119929, an oral prodrug of a targeted cancer immunotherapy TLR7 agonist. Xenobiotica 2022; 52:855-867. [PMID: 36004550 DOI: 10.1080/00498254.2022.2116368] [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: 10/15/2022]
Abstract
RO7119929 is being developed as an orally administered prodrug of the TLR7-specific agonist and active drug, RO7117418, for the treatment of patients with solid tumours.In this publication, we present a case study wherein the human pharmacokinetics and pharmacological active dose were prospectively predicted following oral administration of the prodrug.A simple translational pharmacokinetic-pharmacodynamic strategy was applied to predict the pharmacological active dose of the prodrug in human. In vivo studies in monkey showed that an unbound plasma exposure of active drug of 1.5 ng/mL elicited secretion of key serum pharmacodynamic cytokine and chemokine biomarkers in monkey. This threshold of 1.5 ng/mL was close to the minimum effective concentration of active drug required to induce cytokine secretion in human peripheral blood mononuclear cells (3 ng/mL).Measured in vitro physicochemical and biochemical properties of the prodrug and active drug were applied as input parameters in physiologically based pharmacokinetic models to predict the pharmacokinetics of active drug after oral dosing of the prodrug in humans. Then, using the PBPK model, a dose which delivered an unbound plasma Cmax in line with the target pharmacodynamic threshold of 1.5 ng/mL was found. This defined the lowest pharmacologically active dose as 3 mg.The prodrug entered the clinic in 2020 in patients with primary or secondary liver cancers. Clear pharmacodynamic, transient and dose-dependent cytokine induction was observed at prodrug doses >1 mg.
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Affiliation(s)
- Caroline Rynn
- Roche Pharma Research & Early Development pRED, Roche Innovation Center Basel, Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Kenichi Umehara
- Roche Pharma Research & Early Development pRED, Roche Innovation Center Basel, Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Tianyi Jiang
- Roche Innovation Center Shanghai, Roche R&D Center (China) Ltd., 371 Lishizhen Road, Building 5, Pudong, Shanghai 201203, China
| | - Malika Ait-Goughoulte
- Roche Pharma Research & Early Development pRED, Roche Innovation Center Basel, Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Neil Parrott
- Roche Pharma Research & Early Development pRED, Roche Innovation Center Basel, Grenzacherstrasse 124, CH-4070, Basel, Switzerland
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11
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Evidence of the Need for Modified Well-stirred Model in In Vitro to In Vivo Extrapolation. Eur J Pharm Sci 2022; 177:106268. [PMID: 35901930 DOI: 10.1016/j.ejps.2022.106268] [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] [Received: 04/20/2022] [Accepted: 07/01/2022] [Indexed: 11/20/2022]
Abstract
In vitro to in vivo extrapolation (IVIVE), an approach for hepatic clearance (CLH) prediction used worldwide, remains controversial due to systematic underprediction. Among the various probable factors, the original assumption of the hepatic mathematical model (i.e., the well-stirred model, WSM) may become problematic, leading to the underestimation of drug CLH. Having a similar prerequisite that the well-stirred conditions are homogenous with perfectly mixed reactants, but using a different driving concentration, the modified well-stirred model (MWSM) stands apart from the WSM. However, we believe that both models should coexist so that the entire well-stirred scenario can be completely illustrated. Consequently, we collected published data from the literature and employed a logistic regression method to differentiate the optimal timing of use between WSM and MWSM in drug CLH prediction. Generally, variances adopted in the regression, including partition coefficient (logP), fraction unbound (fu), volumes of distribution at steady-state (Vss), and mean residence time (MRT), corresponded to our assumption when protein-facilitated uptake was considered. Furthermore, a new empirical approach was introduced to allow practical use of the MWSM. The results showed that this model could provide a more precise prediction compared to previous empirical approaches. Therefore, these preliminary results not only delineated a more detailed structure and mechanism of MWSM but also highlighted its necessity and potential.
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12
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Petersson C, Zhou X, Berghausen J, Cebrian D, Davies M, DeMent K, Eddershaw P, Riedmaier AE, Leblanc AF, Manveski N, Marathe P, Mavroudis PD, McDougall R, Parrott N, Reichel A, Rotter C, Tess D, Volak LP, Xiao G, Yang Z, Baker J. Current Approaches for Predicting Human PK for Small Molecule Development Candidates: Findings from the IQ Human PK Prediction Working Group Survey. AAPS J 2022; 24:85. [DOI: 10.1208/s12248-022-00735-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 07/05/2022] [Indexed: 11/30/2022] Open
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13
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Jones RS, Leung C, Chang JH, Brown S, Liu N, Yan Z, Kenny JR, Broccatelli F. Application of empirical scalars to enable early prediction of human hepatic clearance using IVIVE in drug discovery: an evaluation of 173 drugs. Drug Metab Dispos 2022; 50:DMD-AR-2021-000784. [PMID: 35636770 DOI: 10.1124/dmd.121.000784] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 04/20/2022] [Accepted: 05/12/2022] [Indexed: 11/22/2022] Open
Abstract
The utilization of in vitro data to predict drug pharmacokinetics (PK) in vivo has been a consistent practice in early drug discovery for decades. However, its success is hampered by mispredictions attributed to uncharacterized biological phenomena/experimental artifacts. Predicted drug clearance (CL) from experimental data (i.e. hepatocyte intrinsic clearance: CLint, fraction unbound in plasma: fu,p) is often systematically underpredicted using the well-stirred model (WSM). The objective of this study was to evaluate using empirical scalars in the WSM to correct for CL mispredictions. Drugs (N=28) were used to generate numerical scalars on CLint (α), and fu,p (β) to minimize the error (AAFE) for CL predictions. These scalars were validated using an additional dataset (N=28 drugs) and applied to a non-redundant AstraZeneca (AZ) dataset available in the literature (N=117 drugs) for a total of 173 compounds. CL predictions using the WSM were improved for most compounds using an α value of 3.66 (~64%<2-fold) compared to no scaling (~46%<2-fold). Similarly, using a β value of 0.55 or combination of α and β scalars (values of 1.74 and 0.66, respectively) resulted in a similar improvement in predictions (~64%<2-fold and ~65%<2-fold, respectively). For highly bound compounds (fu,p{less than or equal to}0.01), AAFE was substantially reduced across all scaling methods. Using the β scalar alone or a combination of α and β appeared optimal; and produce larger magnitude corrections for highly-bound compounds. Some drugs are still disproportionally mispredicted, however the improvements in prediction error and simplicity of applying these scalars suggests its utility for early-stage CL predictions. Significance Statement In early drug discovery, prediction of human clearance using in vitro experimental data plays an essential role in triaging compounds prior to in vivo studies. These predictions have been systematically underestimated. Here we introduce empirical scalars calibrated on the extent of plasma protein binding that appear to improve clearance prediction across multiple datasets. This approach can be used in early phases of drug discovery prior to the availability of pre-clinical data for early quantitative predictions of human clearance.
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Affiliation(s)
| | | | - Jae H Chang
- Preclinical Development Sciences, ORIC Pharmaceuticals, United States
| | | | | | | | - Jane R Kenny
- Drug Metabolism & Pharmacokinetics, Genentech Inc, United States
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14
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Naga D, Parrott N, Ecker GF, Olivares-Morales A. Evaluation of the Success of High-Throughput Physiologically Based Pharmacokinetic (HT-PBPK) Modeling Predictions to Inform Early Drug Discovery. Mol Pharm 2022; 19:2203-2216. [PMID: 35476457 PMCID: PMC9257750 DOI: 10.1021/acs.molpharmaceut.2c00040] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
![]()
Minimizing in vitro and in vivo testing
in early drug discovery
with the use of physiologically based pharmacokinetic (PBPK) modeling
and machine learning (ML) approaches has the potential to reduce discovery
cycle times and animal experimentation. However, the prediction success
of such an approach has not been shown for a larger and diverse set
of compounds representative of a lead optimization pipeline. In this
study, the prediction success of the oral (PO) and intravenous (IV)
pharmacokinetics (PK) parameters in rats was assessed using a “bottom-up”
approach, combining in vitro and ML inputs with a PBPK model. More
than 240 compounds for which all of the necessary inputs and PK data
were available were used for this assessment. Different clearance
scaling approaches were assessed, using hepatocyte intrinsic clearance
and protein binding as inputs. In addition, a novel high-throughput
PBPK (HT-PBPK) approach was evaluated to assess the scalability of
PBPK predictions for a larger number of compounds in drug discovery.
The results showed that bottom-up PBPK modeling was able to predict
the rat IV and PO PK parameters for the majority of compounds within
a 2- to 3-fold error range, using both direct scaling and dilution
methods for clearance predictions. The use of only ML-predicted inputs
from the structure did not perform well when using in vitro inputs,
likely due to clearance miss predictions. The HT-PBPK approach produced
comparable results to the full PBPK modeling approach but reduced
the simulation time from hours to seconds. In conclusion, a bottom-up
PBPK and HT-PBPK approach can successfully predict the PK parameters
and guide early discovery by informing compound prioritization, provided
that good in vitro assays are in place for key parameters such as
clearance.
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Affiliation(s)
- Doha Naga
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland.,Department of Pharmaceutical Sciences, University of Vienna, 1090 Vienna, Austria
| | - Neil Parrott
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Gerhard F Ecker
- Department of Pharmaceutical Sciences, University of Vienna, 1090 Vienna, Austria
| | - Andrés Olivares-Morales
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland
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15
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Walles M, Pähler A, Isin EM, Weidolf L. Meeting report of the second European Biotransformation Workshop. Xenobiotica 2022; 52:426-431. [PMID: 35410573 DOI: 10.1080/00498254.2022.2064253] [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: 10/18/2022]
Abstract
Challenges and opportunities in the field of biotransformation were presented and discussed at the 2nd European Biotransformation workshop which was conducted virtually in collaboration with the DMDG on November 24/25, 2021. Here we summarise the presentations and discussions from this workshop.The following topics were covered:Regulatory requirements and biotransformation studies for antibody drug conjugates (ADCs) and antisense oligonucleotides (ASOs)Solutions for mass spectral data processing of peptides and oligonucleotidesFuture outsourcing needs in biotransformation for new modalitiesEstablished quantitative and qualitative workflows for metabolite identificationNew in vitro systems to study new chemical entities (NCEs) with low metabolic turnoverNew strategies on the timing of the human ADME (absorption, distribution, metabolism, excretion) study and to investigate the impact of human microbiome on drug development.
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Affiliation(s)
- M Walles
- Department, a Pharmacokinetic Sciences, Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - A Pähler
- Pharma Research and Early Development, F. Hoffmann-La Roche
| | - E M Isin
- DMPK, Translational Medicine, Servier, Orléans, France
| | - L Weidolf
- Drug Metabolism and Pharmacokinetics, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
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16
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Docci L, Milani N, Ramp T, Romeo AA, Godoy P, Franyuti DO, Krähenbühl S, Gertz M, Galetin A, Parrott N, Fowler S. Exploration and application of a liver-on-a-chip device in combination with modelling and simulation for quantitative drug metabolism studies. LAB ON A CHIP 2022; 22:1187-1205. [PMID: 35107462 DOI: 10.1039/d1lc01161h] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Microphysiological systems (MPS) are complex and more physiologically realistic cellular in vitro tools that aim to provide more relevant human in vitro data for quantitative prediction of clinical pharmacokinetics while also reducing the need for animal testing. The PhysioMimix liver-on-a-chip integrates medium flow with hepatocyte culture and has the potential to be adopted for in vitro studies investigating the hepatic disposition characteristics of drug candidates. The current study focusses on liver-on-a-chip system exploration for multiple drug metabolism applications. Characterization of cytochrome P450 (CYP), UDP-glucuronosyl transferase (UGT) and aldehyde oxidase (AO) activities was performed using 15 drugs and in vitro to in vivo extrapolation (IVIVE) was assessed for 12 of them. Next, the utility of the liver-on-a-chip for estimation of the fraction metabolized (fm) via specific biotransformation pathways of quinidine and diclofenac was established. Finally, the metabolite identification opportunities were also explored using efavirenz as an example drug with complex primary and secondary metabolism involving a combination of CYP, UGT and sulfotransferase enzymes. A key aspect of these investigations was the application of mathematical modelling for improved parameter calculation. Such approaches will be required for quantitative assessment of metabolism and/or transporter processes in systems where medium flow and system compartments result in non-homogeneous drug concentrations. In particular, modelling was used to explore the effect of evaporation from the medium and it was found that the intrinsic clearance (CLint) might be underestimated by up to 40% for low clearance compounds if evaporation is not accounted for. Modelling of liver-on-a-chip in vitro data also enhanced the approach to fm estimation allowing objective assessment of metabolism models of different complexity. The resultant diclofenac fm,UGT of 0.64 was highly comparable with values reported previously in the literature. The current study demonstrates the integration of mathematical modelling with experimental liver-on-a-chip studies and illustrates how this approach supports generation of high quality of data from complex in vitro cellular systems.
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Affiliation(s)
- Luca Docci
- Pharmaceutical Sciences, Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland.
- Clinical Pharmacology & Toxicology, University Hospital, Schanzenstrasse 55, 4031, Basel, Switzerland
| | - Nicolò Milani
- Pharmaceutical Sciences, Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland.
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, UK
| | - Thomas Ramp
- Pharmaceutical Sciences, Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland.
| | - Andrea A Romeo
- Pharmaceutical Sciences, Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland.
| | - Patricio Godoy
- Pharmaceutical Sciences, Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland.
| | - Daniela Ortiz Franyuti
- Pharmaceutical Sciences, Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland.
| | - Stephan Krähenbühl
- Clinical Pharmacology & Toxicology, University Hospital, Schanzenstrasse 55, 4031, Basel, Switzerland
| | - Michael Gertz
- Pharmaceutical Sciences, Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland.
| | - Aleksandra Galetin
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, UK
| | - Neil Parrott
- Pharmaceutical Sciences, Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland.
| | - Stephen Fowler
- Pharmaceutical Sciences, Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland.
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17
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Klammers F, Goetschi A, Ekiciler A, Walter I, Parrott N, Fowler S, Umehara K. Estimation of fraction metabolized by cytochrome P450 (CYP) enzymes using long-term co-cultured human hepatocytes. Drug Metab Dispos 2022; 50:566-575. [PMID: 35246464 DOI: 10.1124/dmd.121.000765] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 02/25/2022] [Indexed: 11/22/2022] Open
Abstract
Estimation of the fraction of a drug metabolized by individual hepatic cytochrome P450 (CYP) enzymes relative to hepatic metabolism (fm,CYP) or total clearance (fCL,CYP) has been challenging for low turnover compounds due to insufficient resolution of the intrinsic clearance (CLint) measurement in vitro and difficulties in quantifying the formation of low abundance metabolites. To overcome this gap, inhibition of drug depletion or selective metabolite formation for 7 marker CYP substrates was investigated using chemical inhibitors and a micro-patterned hepatocyte co-culture system (HepatoPac®). The use of 3 µM itraconazole was successfully validated for estimation of fm,CYP3A4 by demonstration of fm values within a 2-fold of in vivo estimates for 10 out of 13 CYP3A4 substrates in a reference set of marketed drugs. Other CYP3A4 inhibitors (ketoconazole and posaconazole) were not optimal for estimation of fm,CYP3A4 for low turnover compounds due to their high CLint. The current study also demonstrated that selective inhibition sufficient for fm calculation was achieved by inhibitors of CYP1A2 (20 µM furafylline), CYP2C8 (40 µM montelukast), CYP2C9 (40 µM sulfaphenazole), CYP2C19 (3 µM (-)N-3-benzyl-phenobarbital) and CYP2D6 (5 µM quinidine). Good estimation of fm,CYP2B6 was not possible in this study due to the poor selectivity of the tested inhibitor (20 µM ticlopidine). The approach verified in this study can result in an improved fm estimation which is aligned with the regulatory agencies' guidance and can support a victim drug-drug interaction risk assessment strategy for low clearance discovery and development drug candidates. Significance Statement Successful qualification of a chemical inhibition assay for estimation of fraction metabolized requires chemical inhibitors which retain sufficient unbound concentrations over time in the incubates. The current co-cultured hepatocyte assay enabled estimation of fraction metabolized, especially by CYP3A4, during the drug discovery phase where metabolite quantification methods may not be available. The method enables the assessment of PK variability and victim DDI risks due to enzyme polymorphism or inhibition/induction with more confidence, especially for low clearance drug candidates.
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Affiliation(s)
| | | | - Aynur Ekiciler
- Pharmaceutical Sciences, F. Hoffmann-LaRoche, Switzerland
| | | | | | | | - Kenichi Umehara
- Pharmaceutical Sciences, Roche Pharmaceutical Research and Early Development, Switzerland
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18
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Youhanna S, Kemas AM, Preiss L, Zhou Y, Shen JX, Cakal SD, Paqualini FS, Goparaju SK, Shafagh RZ, Lind JU, Sellgren CM, Lauschke VM. Organotypic and Microphysiological Human Tissue Models for Drug Discovery and Development-Current State-of-the-Art and Future Perspectives. Pharmacol Rev 2022; 74:141-206. [PMID: 35017176 DOI: 10.1124/pharmrev.120.000238] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 10/12/2021] [Indexed: 12/11/2022] Open
Abstract
The number of successful drug development projects has been stagnant for decades despite major breakthroughs in chemistry, molecular biology, and genetics. Unreliable target identification and poor translatability of preclinical models have been identified as major causes of failure. To improve predictions of clinical efficacy and safety, interest has shifted to three-dimensional culture methods in which human cells can retain many physiologically and functionally relevant phenotypes for extended periods of time. Here, we review the state of the art of available organotypic culture techniques and critically review emerging models of human tissues with key importance for pharmacokinetics, pharmacodynamics, and toxicity. In addition, developments in bioprinting and microfluidic multiorgan cultures to emulate systemic drug disposition are summarized. We close by highlighting important trends regarding the fabrication of organotypic culture platforms and the choice of platform material to limit drug absorption and polymer leaching while supporting the phenotypic maintenance of cultured cells and allowing for scalable device fabrication. We conclude that organotypic and microphysiological human tissue models constitute promising systems to promote drug discovery and development by facilitating drug target identification and improving the preclinical evaluation of drug toxicity and pharmacokinetics. There is, however, a critical need for further validation, benchmarking, and consolidation efforts ideally conducted in intersectoral multicenter settings to accelerate acceptance of these novel models as reliable tools for translational pharmacology and toxicology. SIGNIFICANCE STATEMENT: Organotypic and microphysiological culture of human cells has emerged as a promising tool for preclinical drug discovery and development that might be able to narrow the translation gap. This review discusses recent technological and methodological advancements and the use of these systems for hit discovery and the evaluation of toxicity, clearance, and absorption of lead compounds.
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Affiliation(s)
- Sonia Youhanna
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
| | - Aurino M Kemas
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
| | - Lena Preiss
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
| | - Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
| | - Joanne X Shen
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
| | - Selgin D Cakal
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
| | - Francesco S Paqualini
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
| | - Sravan K Goparaju
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
| | - Reza Zandi Shafagh
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
| | - Johan Ulrik Lind
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
| | - Carl M Sellgren
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
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Fowler S, Brink A, Cleary Y, Guenther A, Heinig K, Husser C, Kletzl H, Kratochwil NA, Mueller L, Savage M, Stillhart C, Tuerck DW, Ullah M, Umehara K, Poirier A. Addressing today's ADME challenges in the translation of in vitro absorption, distribution, metabolism and excretion characteristics to human: A case study of the SMN2 mRNA splicing modifier risdiplam. Drug Metab Dispos 2021; 50:65-75. [PMID: 34620695 DOI: 10.1124/dmd.121.000563] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 09/30/2021] [Indexed: 11/22/2022] Open
Abstract
Small molecules that present complex absorption, distribution, metabolism, and elimination (ADME) properties can be challenging to investigate as potential therapeutics. Acquiring data through standard methods can yield results that are insufficient to describe the in vivo situation, which can affect downstream development decisions. Implementing in vitro - in vivo - in silico strategies throughout the drug development process is effective in identifying and mitigating risks while speeding up their development. Risdiplam (EVRYSDI®) - an orally bioavailable, small molecule approved by the U.S. Food and Drug Administration and more recently by the European Medicines Agency for the treatment of patients {greater than or equal to}2 months of age with spinal muscular atrophy (SMA), is presented here as a case study. Risdiplam is a low turnover compound whose metabolism is mediated through a non-cytochrome P450 enzymatic pathway. Four main challenges of risdiplam are discussed: predicting in vivo hepatic clearance, determining in vitro metabolites with regard to metabolites in safety testing guidelines, elucidating enzymes responsible for clearance, and estimating potential drug-drug interactions. A combination of in vitro and in vivo results was successfully extrapolated and used to develop a robust physiologically based pharmacokinetic model of risdiplam. These results were verified through early clinical studies, further strengthening the understanding of the ADME properties of risdiplam in humans. These approaches can be applied to other compounds with similar ADME profiles, which may be difficult to investigate using standard methods. Significance Statement Risdiplam is the first approved, small molecule, survival of motor neuron 2 mRNA splicing modifier for the treatment of spinal muscular atrophy. The approach taken to characterize the absorption, distribution, metabolism and excretion (ADME) properties of risdiplam during clinical development incorporated in vitro-in vivo-in silico techniques, which may be applicable to other small molecules with challenging ADME. These strategies may be useful in improving the speed at which future drug molecules can be developed.
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Affiliation(s)
| | - Andreas Brink
- Pharmaceutical Sciences, Roche Pharma Research and Early Development, F. Hoffmann-La Roche Ltd, Switzerland
| | - Yumi Cleary
- Pharmaceutical Sciences, Roche Pharma Research and Early Development, F. Hoffmann-La Roche Ltd, Switzerland
| | - Andreas Guenther
- Pharmaceutical Sciences, Roche Pharma Research and Early Development, F. Hoffmann-La Roche Ltd, Switzerland
| | - Katja Heinig
- Pharmaceutical Sciences, Roche Pharma Research and Early Development, F. Hoffmann-La Roche Ltd, Switzerland
| | | | - Heidemarie Kletzl
- Pharmaceutical Sciences, Roche Pharma Research and Early Development, F. Hoffmann-La Roche Ltd, Switzerland
| | | | - Lutz Mueller
- Pharmaceutical Sciences, Roche Pharma Research and Early Development, F. Hoffmann-La Roche Ltd, Switzerland
| | - Mark Savage
- Unilabs York Bioanalytical Solutions, United Kingdom
| | - Cordula Stillhart
- Formulation & Process Sciences, Pharmaceutical R&D, F. Hoffmann-La Roche Ltd, Switzerland
| | | | - Mohammed Ullah
- Pharmaceutical Sciences, Roche Pharmaceutical Research and Early Development, Switzerland
| | - Kenichi Umehara
- Pharmaceutical Sciences, Roche Pharmaceutical Research and Early Development, Switzerland
| | - Agnès Poirier
- Pharmaceutical Sciences, F.Hoffmann-La Roche, Switzerland
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20
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Monckton CP, Brown GE, Khetani SR. Latest impact of engineered human liver platforms on drug development. APL Bioeng 2021; 5:031506. [PMID: 34286173 PMCID: PMC8286174 DOI: 10.1063/5.0051765] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 06/21/2021] [Indexed: 01/07/2023] Open
Abstract
Drug-induced liver injury (DILI) is a leading cause of drug attrition, which is partly due to differences between preclinical animals and humans in metabolic pathways. Therefore, in vitro human liver models are utilized in biopharmaceutical practice to mitigate DILI risk and assess related mechanisms of drug transport and metabolism. However, liver cells lose phenotypic functions within 1–3 days in two-dimensional monocultures on collagen-coated polystyrene/glass, which precludes their use to model the chronic effects of drugs and disease stimuli. To mitigate such a limitation, bioengineers have adapted tools from the semiconductor industry and additive manufacturing to precisely control the microenvironment of liver cells. Such tools have led to the fabrication of advanced two-dimensional and three-dimensional human liver platforms for different throughput needs and assay endpoints (e.g., micropatterned cocultures, spheroids, organoids, bioprinted tissues, and microfluidic devices); such platforms have significantly enhanced liver functions closer to physiologic levels and improved functional lifetime to >4 weeks, which has translated to higher sensitivity for predicting drug outcomes and enabling modeling of diseased phenotypes for novel drug discovery. Here, we focus on commercialized engineered liver platforms and case studies from the biopharmaceutical industry showcasing their impact on drug development. We also discuss emerging multi-organ microfluidic devices containing a liver compartment that allow modeling of inter-tissue crosstalk following drug exposure. Finally, we end with key requirements for engineered liver platforms to become routine fixtures in the biopharmaceutical industry toward reducing animal usage and providing patients with safe and efficacious drugs with unprecedented speed and reduced cost.
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Affiliation(s)
- Chase P Monckton
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois 60607, USA
| | - Grace E Brown
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois 60607, USA
| | - Salman R Khetani
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois 60607, USA
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21
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Yadav J, El Hassani M, Sodhi J, Lauschke VM, Hartman JH, Russell LE. Recent developments in in vitro and in vivo models for improved translation of preclinical pharmacokinetics and pharmacodynamics data. Drug Metab Rev 2021; 53:207-233. [PMID: 33989099 DOI: 10.1080/03602532.2021.1922435] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Improved pharmacokinetics/pharmacodynamics (PK/PD) prediction in the early stages of drug development is essential to inform lead optimization strategies and reduce attrition rates. Recently, there have been significant advancements in the development of new in vitro and in vivo strategies to better characterize pharmacokinetic properties and efficacy of drug leads. Herein, we review advances in experimental and mathematical models for clearance predictions, advancements in developing novel tools to capture slowly metabolized drugs, in vivo model developments to capture human etiology for supporting drug development, limitations and gaps in these efforts, and a perspective on the future in the field.
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Affiliation(s)
- Jaydeep Yadav
- Department of Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co., Inc., Boston, MA, USA
| | | | - Jasleen Sodhi
- Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Jessica H Hartman
- Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA
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