1
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Broccatelli F, Veeravalli V, Cashion D, Baylon JL, Lombardo F, Jia L. Application of Mechanistic Multiparameter Optimization and Large-Scale In Vitro to In Vivo Pharmacokinetics Correlations to Small-Molecule Therapeutic Projects. Mol Pharm 2024; 21:4312-4323. [PMID: 39135316 DOI: 10.1021/acs.molpharmaceut.4c00256] [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: 09/03/2024]
Abstract
Computational chemistry and machine learning are used in drug discovery to predict the target-specific and pharmacokinetic properties of molecules. Multiparameter optimization (MPO) functions are used to summarize multiple properties into a single score, aiding compound prioritization. However, over-reliance on subjective MPO functions risks reinforcing human bias. Mechanistic modeling approaches based on physiological relevance can be adapted to meet different potential key objectives of the project (e.g., minimizing dose, maximizing safety margins, and/or minimizing drug-drug interaction risk) while retaining the same underlying model structure. The current work incorporates recent approaches to predict in vivo pharmacokinetic (PK) properties and validates in vitro to in vivo correlation analysis to support mechanistic PK MPO. Examples of use and impact in small-molecule drug discovery projects are provided. Overall, the mechanistic MPO identifies 83% of the compounds considered as short-listed for clinical experiments in the top second percentile, and 100% in the top 10th percentile, resulting in an area under the receiver operating characteristic curve (AUCROC) > 0.95. In addition, the MPO score successfully recapitulates the chronological progression of the optimization process across different scaffolds. Finally, the MPO scores for compounds characterized in pharmacokinetics experiments are markedly higher compared with the rest of the compounds being synthesized, highlighting the potential of this tool to reduce the reliance on in vivo testing for compound screening.
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Affiliation(s)
- Fabio Broccatelli
- Bristol-Myers Squibb Company, San Diego, California 92121, United States
| | | | - Daniel Cashion
- Bristol-Myers Squibb Company, San Diego, California 92121, United States
| | - Javier L Baylon
- Bristol-Myers Squibb Company, San Diego, California 92121, United States
| | - Franco Lombardo
- CmaxDMPK LLC, Framingham, Massachusetts 01701, United States
| | - Lei Jia
- Bristol-Myers Squibb Company, San Diego, California 92121, United States
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2
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Li H, Bunglawala F, Hewitt NJ, Pendlington R, Cubberley R, Nicol B, Spriggs S, Baltazar M, Cable S, Dent M. ADME characterization and PBK model development of 3 highly protein-bound UV filters through topical application. Toxicol Sci 2023; 196:1-15. [PMID: 37584694 PMCID: PMC10613959 DOI: 10.1093/toxsci/kfad081] [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] [Indexed: 08/17/2023] Open
Abstract
Estimating human exposure in the safety assessment of chemicals is crucial. Physiologically based kinetic (PBK) models which combine information on exposure, physiology, and chemical properties, describing the absorption, distribution, metabolism, and excretion (ADME) processes of a chemical, can be used to calculate internal exposure metrics such as maximum concentration and area under the concentration-time curve in plasma or tissues of a test chemical in next-generation risk assessment. This article demonstrates the development of PBK models for 3 UV filters, specifically octyl methoxycinnamate, octocrylene, and 4-methylbenzylidene camphor. The models were parameterized entirely based on data obtained from in vitro and/or in silico methods in a bottom-up modeling approach and then validated based on human dermal pharmacokinetic (PK) data. The 3 UV filters are "difficult to test" in in vitro test systems due to high lipophilicity, high binding affinity for proteins, and nonspecific binding, for example, toward plastic. This research work presents critical considerations in ADME data generation, interpretation, and parameterization to assure valid PBK model development to increase confidence in using PBK modeling to help make safety decisions in the absence of human PK data. The developed PBK models of the 3 chemicals successfully simulated the plasma concentration profiles of clinical PK data following dermal application, indicating the reliability of the ADME data generated and the parameters determined. The study also provides insights and lessons learned for characterizing ADME and developing PBK models for highly lipophilic and protein-bound chemicals in the future.
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Affiliation(s)
- Hequn Li
- Unilever Safety and Environmental Assurance Centre, Sharnbrook MK44 1LQ, UK
| | - Fazila Bunglawala
- Unilever Safety and Environmental Assurance Centre, Sharnbrook MK44 1LQ, UK
| | | | - Ruth Pendlington
- Unilever Safety and Environmental Assurance Centre, Sharnbrook MK44 1LQ, UK
| | - Richard Cubberley
- Unilever Safety and Environmental Assurance Centre, Sharnbrook MK44 1LQ, UK
| | - Beate Nicol
- Unilever Safety and Environmental Assurance Centre, Sharnbrook MK44 1LQ, UK
| | - Sandrine Spriggs
- Unilever Safety and Environmental Assurance Centre, Sharnbrook MK44 1LQ, UK
| | - Maria Baltazar
- Unilever Safety and Environmental Assurance Centre, Sharnbrook MK44 1LQ, UK
| | - Sophie Cable
- Unilever Safety and Environmental Assurance Centre, Sharnbrook MK44 1LQ, UK
| | - Matthew Dent
- Unilever Safety and Environmental Assurance Centre, Sharnbrook MK44 1LQ, UK
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3
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Schulz JA, Stresser DM, Kalvass JC. Plasma Protein-Mediated Uptake and Contradictions to the Free Drug Hypothesis: A Critical Review. Drug Metab Rev 2023:1-34. [PMID: 36971325 DOI: 10.1080/03602532.2023.2195133] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
According to the free drug hypothesis (FDH), only free, unbound drug is available to interact with biological targets. This hypothesis is the fundamental principle that continues to explain the vast majority of all pharmacokinetic and pharmacodynamic processes. Under the FDH, the free drug concentration at the target site is considered the driver of pharmacodynamic activity and pharmacokinetic processes. However, deviations from the FDH are observed in hepatic uptake and clearance predictions, where observed unbound intrinsic hepatic clearance (CLint,u) is larger than expected. Such deviations are commonly observed when plasma proteins are present and form the basis of the so-called plasma protein-mediated uptake effect (PMUE). This review will discuss the basis of plasma protein binding as it pertains to hepatic clearance based on the FDH, as well as several hypotheses that may explain the underlying mechanisms of PMUE. Notably, some, but not all, potential mechanisms remained aligned with the FDH. Finally, we will outline possible experimental strategies to elucidate PMUE mechanisms. Understanding the mechanisms of PMUE and its potential contribution to clearance underprediction is vital to improving the drug development process.
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4
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Physiologically Based Pharmacokinetic Modeling To Guide Management of Drug Interactions between Elexacaftor-Tezacaftor-Ivacaftor and Antibiotics for the Treatment of Nontuberculous Mycobacteria. Antimicrob Agents Chemother 2022; 66:e0110422. [PMID: 36286508 PMCID: PMC9664863 DOI: 10.1128/aac.01104-22] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Nontuberculous mycobacteria (NTM) are the pathogens of concern in people with cystic fibrosis (pwCF) due to their association with deterioration of lung function. Treatment requires the use of a multidrug combination regimen, creating the potential for drug-drug interactions (DDIs) with cystic fibrosis transmembrane conductance regulator (CFTR)-modulating therapies, including elexacaftor, tezacaftor, and ivacaftor (ETI), which are eliminated mainly through cytochrome P450 (CYP) 3A-mediated metabolism. An assessment of the DDI risk for ETI coadministered with NTM treatments, including rifabutin, clofazimine, and clarithromycin, is needed to provide appropriate guidance on dosing.
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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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6
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Gardner I, Xu M, Han C, Wang Y, Jiao X, Jamei M, Khalidi H, Kilford P, Neuhoff S, Southall R, Turner DB, Musther H, Jones B, Taylor S. Non-specific binding of compounds in in vitro metabolism assays: a comparison of microsomal and hepatocyte binding in different species and an assessment of the accuracy of prediction models. Xenobiotica 2022; 52:943-956. [PMID: 36222269 DOI: 10.1080/00498254.2022.2132426] [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: 12/24/2022]
Abstract
Non-specific binding in in vitro metabolism systems leads to an underestimation of the true intrinsic metabolic clearance of compounds being studied. Therefore in vitro binding needs to be accounted for when extrapolating in vitro data to predict the in vivo metabolic clearance of a compound. While techniques exist for experimentally determining the fraction of a compound unbound in in vitro metabolism systems, early in drug discovery programmes computational approaches are often used to estimate the binding in the in vitro system.Experimental fraction unbound data (n = 60) were generated in liver microsomes (fumic) from five commonly used pre-clinical species (rat, mouse, dog, minipig, monkey) and humans. Unbound fraction in incubations with mouse, rat or human hepatocytes was determined for the same 60 compounds. These data were analysed to determine the relationship between experimentally determined binding in the different matrices and across different species. In hepatocytes there was a good correlation between fraction unbound in human and rat (r2=0.86) or mouse (r2=0.82) hepatocytes. Similar correlations were observed between binding in human liver microsomes and microsomes from rat, mouse, dog, Göttingen minipig or monkey liver microsomes (r2 of >0.89, n = 51 - 52 measurements in different species). Physicochemical parameters (logP, pKa and logD) were predicted for all evaluated compounds. In addition, logP and/or logD were measured for a subset of compounds.Binding to human hepatocytes predicted using 5 different methods was compared to the measured data for a set of 59 compounds. The best methods evaluated used measured microsomal binding in human liver microsomes to predict hepatocyte binding. The collated physicochemical data were used to predict the human fumic using four different in silico models for a set of 53-60 compounds. The correlation (r2) and root mean square error between predicted and observed microsomal binding was 0.69 & 0.20, 0.47 & 0.23, 0.56 & 0.21 and 0.54 & 0.26 for the Turner-Simcyp, Austin, Hallifax-Houston and Poulin models, respectively. These analyses were extended to include measured literature values for binding in human liver microsomes for a larger set of compounds (n=697). For the larger dataset of compounds, microsomal binding was well predicted for neutral compounds (r2=0.67 - 0.70) using the Poulin, Austin, or Turner-Simcyp methods but not for acidic or basic compounds (r2<0.5) using any of the models. While the lipophilicity-based models can be used, the in vitro binding should be measured for compounds where more certainty is needed, using appropriately calibrated assays and possibly established weak, moderate, and strong binders as reference compounds to allow comparison across databases.
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Affiliation(s)
| | - Mandy Xu
- Pharmaron Beijing Co. Ltd., Beijing, China
| | | | - Yi Wang
- Pharmaron Beijing Co. Ltd., Beijing, China
| | | | | | | | - Peter Kilford
- Certara UK Ltd., Sheffield, United Kingdom.,Labcorp Drug Development, Harrogate, United Kingdom
| | | | | | | | | | - Barry Jones
- Pharmaron UK, Hoddesdon, Hertfordshire, United Kingdom
| | - Simon Taylor
- Pharmaron UK, Hoddesdon, Hertfordshire, United Kingdom
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7
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Toselli F, Golding M, Nicolaï J, Gillent E, Chanteux H. Drug clearance by aldehyde oxidase: can we avoid clinical failure? Xenobiotica 2022; 52:890-903. [PMID: 36170034 DOI: 10.1080/00498254.2022.2129519] [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: 01/19/2023]
Abstract
Despite increased awareness of aldehyde oxidase (AO) as a major drug-metabolising enzyme, predicting the pharmacokinetics of its substrates remains challenging. Several drug candidates have been terminated due to high clearance, which were subsequently discovered to be AO substrates. Even retrospective extrapolation of human clearance, from models more sensitive to AO activity, often resulted in underprediction.The questions of the current work thus were: Is there an acceptable degree of in vitro AO metabolism that does not result in high in vivo human clearance? And, if so, how can this be predicted?We built an in vitro/in vivo correlation using known AO substrates, combining multiple in vitro parameters to calculate the blood metabolic clearance mediated by AO (CLbAO). This value was compared with observed blood clearance (CLb-obs), establishing cut-off CLbAO values, to discriminate between low and high CLb-obs. The model was validated using additional literature compounds, and CLb-obs was predicted in the correct category.This simple, categorical, semi-quantitative yet multi-factorial model is readily applicable in drug discovery. Further, it is valuable for high-clearance compounds, as it predicts the CLb group, rather than an exact CLb value, for the substrates of this poorly-characterised enzyme.
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Affiliation(s)
| | | | - Johan Nicolaï
- Development Science, UCB Biopharma, Braine-l'Alleud, Belgium
| | - Eric Gillent
- Development Science, UCB Biopharma, Braine-l'Alleud, Belgium
| | - Hugues Chanteux
- Development Science, UCB Biopharma, Braine-l'Alleud, Belgium
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8
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Aigbogun OP, Nwabufo CK, Owens MN, Allen KJH, Lee JS, Phenix CP, Krol ES. An HPLC-UV validated bioanalytical method for measurement of in vitro phase 1 kinetics of α-synuclein binding bifunctional compounds. Xenobiotica 2022; 52:916-927. [PMID: 36282181 DOI: 10.1080/00498254.2022.2140315] [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: 12/24/2022]
Abstract
Aggregates of the protein α-synuclein are associated with pathophysiology of Parkinson's disease and are present in Lewy Bodies found in the brains of Parkinson's patients. We previously demonstrated that bifunctional compounds composed of caffeine linked via a six carbon chain to either 1-aminoindan (C8-6-I) or nicotine (C8-6-N) bind α-synuclein and protect yeast cells from α-synuclein mediated toxicity.A critical step in development of positron emission tomography (PET) probes for neurodegenerative diseases is evaluation of their metabolic stability. We determined that C8-6-I, and C8-6-N both undergo phase 1 P450 metabolism in mouse, rat, and human liver microsomes. We utilised this metabolic information to guide the design of fluorinated analogues for use as PET probes and determined that the fluorine in 19F-C8-6-I and 19F-C8-6-N is stable to P450 enzymes.We have developed and validated an analytical HPLC-UV method following FDA and EMA guidelines to measure in vitro phase 1 kinetics of these compounds and determine their Vmax, KM and CLint,u in mouse liver microsomes. We found that C8-6-I and 19F-C8-6-I have a two- to fourfold lower CLint,u than C8-6-N, and 19F-C8-6-N. Our approach shows a simple, specific, and effective system to design and develop compounds as PET probes.
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Affiliation(s)
- Omozojie P Aigbogun
- Department of Chemistry, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Chukwunonso K Nwabufo
- Drug Discovery and Development Research Group, College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Madeline N Owens
- Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Kevin J H Allen
- Drug Discovery and Development Research Group, College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Jeremy S Lee
- Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Christopher P Phenix
- Department of Chemistry, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Ed S Krol
- Drug Discovery and Development Research Group, College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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9
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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: 17] [Impact Index Per Article: 8.5] [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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10
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Opportunities and Considerations in the Application of Artificial Intelligence to Pharmacokinetic Prediction. Methods Mol Biol 2022; 2390:461-482. [PMID: 34731483 DOI: 10.1007/978-1-0716-1787-8_21] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
The improvement in the ability of the pharmaceutical industry to predict human pharmacokinetic behavior are attributable to major technological shifts from 1990 to the present day. The opportunity for the application of AI/ML based approaches in the pharmaceutical industry is driven by the abundance of data sets that exist within individual pharmaceutical and biotech companies and the availability, within these environments, of abundant computing power. This chapter seeks to describe opportunities for artificial intelligence to contribute to the assessment and evaluation of the dug metabolism and pharmacokinetic (DMPK) properties of novel compounds across the drug discovery and development continuum. Many initiatives are already underway with respect to the application of AI/ML in predicting pharmacokinetic profiles so the question is not whether AI will influence pharmacokinetic prediction but rather how to best utilize and incorporate this and how to evaluate the value added from these applications. Since our understanding of the underlying biology of the in vitro and in vivo systems with respect to ADME, one of the key challenges to AI-based methods will be the ability to adapt to data sets that change in quality over time.
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Fu S, Yu F, Hu Z, Sun T. Metabolism-Mediated Drug-Drug Interactions – Study Design, Data Analysis, and Implications for In Vitro Evaluations. MEDICINE IN DRUG DISCOVERY 2022. [DOI: 10.1016/j.medidd.2022.100121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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12
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Wang T, Whitcher-Johnstone A, Keith-Luzzi M, Chan TS. HLM-beads: Rapid Assessment of Nonspecific Binding to Human Liver Microsomes Using Magnetizable Beads. Drug Metab Dispos 2021; 49:1056-1062. [PMID: 34561223 DOI: 10.1124/dmd.121.000575] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 09/20/2021] [Indexed: 11/22/2022] Open
Abstract
In early drug development, drug-drug interaction risk is routinely assessed using human liver microsomes (HLMs). Nonspecific binding of drugs to HLMs can affect the determination of accurate enzyme parameters (Km, Ki, KI). Previously, we described a novel in vitro model consisting of HLMs bound to magnetizable beads [HLM-magnetizable-beads system (HLM-beads)]. The HLM-beads enable rapid separation of HLMs from incubation media by applying a magnetic field. Here, HLM-beads were further characterized and evaluated as a tool to assess HLM nonspecific binding of small molecules. The free fractions (fu,mic) of 13 compounds (chosen based on their pKa values) were determined using HLM-beads under three HLM concentrations (0.025, 0.50, and 1.0 mg/ml) and compared with those determined by equilibrium dialysis. Most fu,mic values obtained using HLM-beads were within 0.5- to 2-fold of the values determined using equilibrium dialysis. The highest fold difference were observed for high binders itraconazole and BIRT2584 (1.9- to 2.9-fold), as the pronounced adsorption of these compounds to the equilibrium dialysis apparatus interfered with their fu,mic determination. Correlation and linear regression analysis of the fu,mic values generated using HLM-beads and equilibrium dialysis was conducted. Overall, a good correlation of fu,mic values obtained by the two methods were observed, as the r and R2 values from correlational analysis and linear regression analysis were >0.9 and >0.89, respectively. These studies demonstrate that HLM-beads can produce comparable fu,mic values as determined by equilibrium dialysis while reducing the time required for this type of study from hours to only 10 minutes and compound apparatus adsorption. SIGNIFICANCE STATEMENT: This work introduces a new method of rapidly assessing nonspecific microsomal binding using human liver microsomes bound to magnetizable beads.
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Affiliation(s)
- Ting Wang
- Department of Drug Metabolism and Pharmacokinetics (T.W., A.W.-J., T.S.C.), and Department of Nonclinical Drug Safety (M.K.-L.), Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut
| | - Andrea Whitcher-Johnstone
- Department of Drug Metabolism and Pharmacokinetics (T.W., A.W.-J., T.S.C.), and Department of Nonclinical Drug Safety (M.K.-L.), Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut
| | - Monica Keith-Luzzi
- Department of Drug Metabolism and Pharmacokinetics (T.W., A.W.-J., T.S.C.), and Department of Nonclinical Drug Safety (M.K.-L.), Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut
| | - Tom S Chan
- Department of Drug Metabolism and Pharmacokinetics (T.W., A.W.-J., T.S.C.), and Department of Nonclinical Drug Safety (M.K.-L.), Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut
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13
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Salman MM, Al-Obaidi Z, Kitchen P, Loreto A, Bill RM, Wade-Martins R. Advances in Applying Computer-Aided Drug Design for Neurodegenerative Diseases. Int J Mol Sci 2021; 22:4688. [PMID: 33925236 PMCID: PMC8124449 DOI: 10.3390/ijms22094688] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/26/2021] [Accepted: 04/26/2021] [Indexed: 12/11/2022] Open
Abstract
Neurodegenerative diseases (NDs) including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease are incurable and affect millions of people worldwide. The development of treatments for this unmet clinical need is a major global research challenge. Computer-aided drug design (CADD) methods minimize the huge number of ligands that could be screened in biological assays, reducing the cost, time, and effort required to develop new drugs. In this review, we provide an introduction to CADD and examine the progress in applying CADD and other molecular docking studies to NDs. We provide an updated overview of potential therapeutic targets for various NDs and discuss some of the advantages and disadvantages of these tools.
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Affiliation(s)
- Mootaz M. Salman
- Department of Physiology, Anatomy and Genetics, University of Oxford, Parks Road, Oxford OX1 3QX, UK;
- Oxford Parkinson’s Disease Centre, University of Oxford, South Parks Road, Oxford OX1 3QX, UK
| | - Zaid Al-Obaidi
- Department of Pharmaceutical Chemistry, College of Pharmacy, University of Alkafeel, Najaf 54001, Iraq;
- Department of Chemistry and Biochemistry, College of Medicine, University of Kerbala, Karbala 56001, Iraq
| | - Philip Kitchen
- School of Biosciences, College of Health and Life Sciences, Aston University, Aston Triangle, Birmingham B4 7ET, UK; (P.K.); (R.M.B.)
| | - Andrea Loreto
- Department of Physiology, Anatomy and Genetics, University of Oxford, Parks Road, Oxford OX1 3QX, UK;
- John Van Geest Centre for Brain Repair, University of Cambridge, Cambridge CB2 0PY, UK
| | - Roslyn M. Bill
- School of Biosciences, College of Health and Life Sciences, Aston University, Aston Triangle, Birmingham B4 7ET, UK; (P.K.); (R.M.B.)
| | - Richard Wade-Martins
- Department of Physiology, Anatomy and Genetics, University of Oxford, Parks Road, Oxford OX1 3QX, UK;
- Oxford Parkinson’s Disease Centre, University of Oxford, South Parks Road, Oxford OX1 3QX, UK
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14
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Kwon M, Park JB, Kwon M, Song J, Yeo CS, Bae SH. Pharmacokinetics of 2-phenoxyethanol and its major metabolite, phenoxyacetic acid, after dermal and inhaled routes of exposure: application to development PBPK model in rats. Arch Toxicol 2021; 95:2019-2036. [PMID: 33844041 DOI: 10.1007/s00204-021-03041-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 04/01/2021] [Indexed: 12/20/2022]
Abstract
2-Phenoxyethanol (PE), ethylene glycol monophenyl ether, is widely used as a preservative in cosmetic products as well as in non-cosmetics. Since PE has been used in many types of products, it can be absorbed via dermal or inhaled route for systemic exposures. In this study, the pharmacokinetic (PK) studies of PE and its major metabolite, phenoxyacetic acid (PAA), after dermal (30 mg and 100 mg) and inhaled administration (77 mg) of PE in rats were performed. PE was administered daily for 4 days and blood samples were collected at day 1 and day 4 for PK analysis. PE was rapidly absorbed and extensively metabolized to form PAA. After multiple dosing, the exposures of PE and PAA were decreased presumably due to the induction of metabolizing enzymes of PE and PAA. In dermal mass balance study using [14C]-phenoxyethanol ([14C]PE) as a microtracer, most of the PE and its derivatives were excreted in urine (73.03%) and rarely found in feces (0.66%). Based on these PK results, a whole-body physiologically-based pharmacokinetic (PBPK) model of PE and PAA after dermal application and inhalation in rats was successfully developed. Most of parameters were obtained from the literatures and experiments, and intrinsic clearance at steady-state (CLint,ss) were optimized based on the observed multiple PK data. With the developed model, systemic exposures of PE and PAA after dermal application and inhalation were simulated following no-observed-adverse-effect level (NOAEL) of 500 mg/kg/day for dermal application and that of 12.7 mg/kg/day for inhalation provided by the Environmental Protection Agency. The area under the concentration-time curve at steady state (AUCss) in kidney and liver (and lung for inhalations), which are known target organs of exhibiting toxicity of PE, as well as AUCss in plasma of PE and PAA were obtained from the model.
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Affiliation(s)
- Mihye Kwon
- Korea Institute of Radiological and Medical Sciences Seoul, Nowon-ro 75, Nowon-Gu, Seoul, Korea
| | - Jung Bae Park
- Korea Institute of Radiological and Medical Sciences Seoul, Nowon-ro 75, Nowon-Gu, Seoul, Korea
| | - Miwha Kwon
- Korea Institute of Radiological and Medical Sciences Seoul, Nowon-ro 75, Nowon-Gu, Seoul, Korea
| | - Jinho Song
- Korea Institute of Radiological and Medical Sciences Seoul, Nowon-ro 75, Nowon-Gu, Seoul, Korea
| | - Chang Su Yeo
- Korea Institute of Radiological and Medical Sciences Seoul, Nowon-ro 75, Nowon-Gu, Seoul, Korea
| | - Soo Hyeon Bae
- Korea Institute of Radiological and Medical Sciences Seoul, Nowon-ro 75, Nowon-Gu, Seoul, Korea. .,Q-Fitter Inc., 56-24 Banpo-daero 39-gil, Seocho-gu, Seoul, 06578, Korea.
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15
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Kanebratt KP, Janefeldt A, Vilén L, Vildhede A, Samuelsson K, Milton L, Björkbom A, Persson M, Leandersson C, Andersson TB, Hilgendorf C. Primary Human Hepatocyte Spheroid Model as a 3D In Vitro Platform for Metabolism Studies. J Pharm Sci 2020; 110:422-431. [PMID: 33122050 DOI: 10.1016/j.xphs.2020.10.043] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/19/2020] [Accepted: 10/19/2020] [Indexed: 12/12/2022]
Abstract
3D cultures of primary human hepatocytes (PHH) are emerging as a more in vivo-like culture system than previously available hepatic models. This work describes the characterisation of drug metabolism in 3D PHH spheroids. Spheroids were formed from three different donors of PHH and the expression and activities of important cytochrome P450 enzymes (CYP1A2, 2B6, 2C9, 2D6, and 3A4) were maintained for up to 21 days after seeding. The activity of CYP2B6 and 3A4 decreased, while the activity of CYP2C9 and 2D6 increased over time (P < 0.05). For six test compounds, that are metabolised by multiple enzymes, intrinsic clearance (CLint) values were comparable to standard in vitro hepatic models and successfully predicted in vivo CLint within 3-fold from observed values for low clearance compounds. Remarkably, the metabolic turnover of these low clearance compounds was reproducibly measured using only 1-3 spheroids, each composed of 2000 cells. Importantly, metabolites identified in the spheroid cultures reproduced the major metabolites observed in vivo, both primary and secondary metabolites were captured. In summary, the 3D PHH spheroid model shows promise to be used in drug discovery projects to study drug metabolism, including unknown mechanisms, over an extended period of time.
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Affiliation(s)
- Kajsa P Kanebratt
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden.
| | - Annika Janefeldt
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Liisa Vilén
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Anna Vildhede
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Kristin Samuelsson
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Lucas Milton
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Anders Björkbom
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Marie Persson
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Carina Leandersson
- Physical & Analytical Chemistry, Research and Early Development Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Tommy B Andersson
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Constanze Hilgendorf
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
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16
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Williamson B, Harlfinger S, McGinnity DF. Evaluation of the Disconnect between Hepatocyte and Microsome Intrinsic Clearance and In Vitro In Vivo Extrapolation Performance. Drug Metab Dispos 2020; 48:1137-1146. [DOI: 10.1124/dmd.120.000131] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 08/17/2020] [Indexed: 12/18/2022] Open
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17
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Kosugi Y, Hosea N. Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay. Mol Pharm 2020; 17:2299-2309. [PMID: 32478525 DOI: 10.1021/acs.molpharmaceut.9b01294] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The in vitro-in vivo extrapolation (IVIVE) approach for predicting total plasma clearance (CLtot) has been widely used to rank order compounds early in discovery. More recently, a computational machine learning approach utilizing physicochemical descriptors and fingerprints calculated from chemical structure information has emerged, enabling virtual predictions even earlier in discovery. Previously, this approach focused more on in vitro intrinsic clearance (CLint) prediction. Herein, we directly compare these two approaches for predicting CLtot in rats. A structurally diverse set of 1114 compounds with known in vivo CLtot, in vitro CLint, and plasma protein binding was used as the basis for this evaluation. The machine learning models were assessed by validation approaches using the time- and cluster-split training and test sets, and five-fold cross validation. Assessed by five-fold validation, the random forest regression (RF) and radial basis function (RBF) models demonstrated better prediction performance in eight attempted machine learning models. The CLtot values predicted by the RF and RBF models were within two-fold of the observed values for 67.7 and 71.9% of cluster-split test set compounds, respectively, while the predictivity was worse in the time-split dataset. The predictivity of both models tended to be improved by incorporating in vitro parameters, unbound fraction in plasma (fu,p), and CLint. CLtot prediction utilizing in vitro CLint and the well-stirred model, correcting for the fraction unbound in blood, was substantially worse compared to machine learning approaches for the same cluster-split test set. The reason that CLtot is underestimated by IVIVE is not fully explained by considering the calculated microsomal unbound fraction (cfu,mic), extended clearance classification system (ECCS), and omitting high clearance compounds in excess of hepatic blood flow. The analysis suggests that in silico machine learning models may have the power to reduce reliance on or replace in vitro and in vivo studies for chemical structure optimization in early drug discovery.
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Affiliation(s)
- Yohei Kosugi
- Global DMPK, Takeda California Inc., San Diego, California 92121, United States
| | - Natalie Hosea
- Global DMPK, Takeda California Inc., San Diego, California 92121, United States
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18
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Morita K, Kato M, Kudo T, Ito K. In vitro-in vivo extrapolation of metabolic clearance using human liver microsomes: factors showing variability and their normalization. Xenobiotica 2020; 50:1064-1075. [PMID: 32125203 DOI: 10.1080/00498254.2020.1738592] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
In vitro-in vivo extrapolation (IVIVE) using human liver microsomes has been widely used to predict metabolic clearance, but some of the factors used in the process of prediction show variability for the same compound: notably, microsomal intrinsic clearance values corrected by the unbound fraction (CLint, u), physiological parameters used for scale-up, and the source of in vivo clearance data.The purpose of this study was to assess the correlation between in vitro and in vivo CLint with a focus on factors showing variability using four cytochrome P450 (CYP)3A substrates.We surveyed in vivo clearance values in literature and also determined the microsomal CLint, u values. A scaling factor (SFdirect) was defined as in vivo CLint divided by the microsomal CLint, u, which ranged from 1190 to 2310 (mg protein per kg body weight). The application of a mean SFdirect of 1600 (mg protein per kg body weight) and further normalization by the microsomal CLint, u values of midazolam, the most commonly used substrate, resulted in improved prediction accuracy for CLint, u values from various microsomal batches.The results suggest the normalization of variability might be useful for predicting the in vivo CLint.
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Affiliation(s)
- Keiichi Morita
- Research Institute of Pharmaceutical Sciences, Musashino University, Tokyo, Japan.,Translational Research Division, Chugai Pharmaceutical, Co., Ltd, Kanagawa, Japan
| | - Motohiro Kato
- Research Division, Chugai Pharmaceutical, Co., Ltd, Shizuoka, Japan
| | - Toshiyuki Kudo
- Research Institute of Pharmaceutical Sciences, Musashino University, Tokyo, Japan
| | - Kiyomi Ito
- Research Institute of Pharmaceutical Sciences, Musashino University, Tokyo, Japan
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