Current Approaches for Predicting Human PK for Small Molecule Development Candidates: Findings from the IQ Human PK Prediction Working Group Survey

Petersson, Carl; Zhou, Xin; Berghausen, Joerg; Cebrián, David; Davies, Michael; DeMent, Kevin; Eddershaw, Peter; Riedmaier, Arian Emami; Leblanc, A.; Manveski, Nenad; Marathe, Punit; Mavroudis, Panteleimon D.; McDougall, Robin; Parrott, Neil; Reichel, Andreas; Rotter, Charles J.; Tess, David A.; Volak, Laurie P; Gao, Xiao; Yang, Zheng; Baker, James Α.

doi:10.1208/s12248-022-00735-9

Cited by 18 publications

(15 citation statements)

References 68 publications

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“…It is not surprising to observe IVIVE disconnects in the preclinical and human PK parameter predictions, and common causes for this have been proposed in the literature. Underestimation of CL when using in vitro hepatocyte data for the bottom‐up prediction has been a common challenge across companies (Petersson et al., 2022). Extrinsic factors such as loss of enzymatic activity from the liver tissue preparation process or from storage conditions could contribute and/or the inability of vitro incubations to accurately capture the in vivo hepatic architecture could lead to extrapolation challenges (Chiba et al., 2009; Liu & Pang, 2006).…”

Section: Discussionmentioning

confidence: 99%

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Shared learning from a physiologically based pharmacokinetic modeling strategy for human pharmacokinetics prediction through retrospective analysis of Genentech compounds

Mao

et al. 2023

Biopharm & Drug Disp

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The quantitative prediction of human pharmacokinetics (PK) including the PK profile and key PK parameters are critical for early drug development decisions, successful phase I clinical trials, and the establishment of a range of doses to enable phase II clinical dose selection. Here, we describe an approach employing physiologically based pharmacokinetic (PBPK) modeling (Simcyp) to predict human PK and to validate its performance through retrospective analysis of 18 Genentech compounds for which clinical data are available. In short, physicochemical parameters and in vitro data for preclinical species were integrated using PBPK modeling to predict the in vivo PK observed in mouse, rat, dog, and cynomolgus monkey. Through this process, the in vitro to in vivo extrapolation (IVIVE) was determined and then incorporated into PBPK modeling in order to predict human PK. Overall, the prediction obtained using this PBPK‐IVIVE approach captured the observed human PK profiles of the compounds from the dataset well. The predicted Cmax was within 2‐fold of the observed Cmax for 94% of the compounds while the predicted area under the curve (AUC) was within 2‐fold of the observed AUC for 72% of the compounds. Additionally, important IVIVE trends were revealed through this investigation, including application of scaling factors determined from preclinical IVIVE to human PK prediction for each molecule. Based upon the analysis, this PBPK‐based approach now serves as a practical strategy for human PK prediction at the candidate selection stage at Genentech.

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Section: Discussionmentioning

confidence: 99%

Section: T a B L Ementioning

confidence: 99%

Shared learning from a physiologically based pharmacokinetic modeling strategy for human pharmacokinetics prediction through retrospective analysis of Genentech compounds

Mao

et al. 2023

Biopharm & Drug Disp

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“…Multiple groups have verified the IVIVE approach in their laboratories and confirmed that it delivers superior results to more empirical approaches. ,, Furthermore, the IVIVE can be powerfully combined with physiologically based pharmacokinetic modeling (PBPK) modeling to develop strategies for preclinical and human pharmacokinetic prediction . However, a recent industry survey has shown that although widely used, prospective IVIVE is often not sufficiently accurate and empirical scaling factors are required for human clearance prediction. As the chemical space explored by drug discovery continues to expand, project teams often find themselves beyond the range where IVIVE has been strongly validated (Figure ).…”

Section: Discussionmentioning

confidence: 99%

“…4,7−17 Even then many teams have observed correlation biases, mostly toward underprediction, 14 resulting in a need for various empirical correction factors. 17,18 However, drug discovery teams often use CL p IVIVE prospectively to predict nonclinical and clinical CL p based on in vitro data alone. In the prospective scenario, in vivo elimination pathways are uncertain, meaning that compounds with considerable nonmetabolic elimination and extrahepatic metabolism may be included as well.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Focus on clinically tested compounds may also introduce a positive selection bias, as good nonclinical CL p IVIVE is often a requirement to progress the compound toward the clinics. In this optimized retrospective scenario, the prediction success of CL p IVIVE is usually within 2- to 3-fold error. ,− Even then many teams have observed correlation biases, mostly toward underprediction, resulting in a need for various empirical correction factors. , …”

Section: Introductionmentioning

confidence: 99%

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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)

2023

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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 (f u,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 (P app > 100 nm/s), and moderate observed CLp (5–50 mL/min/kg). Influences of plasma protein binding (f u,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 (P app > 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 discover...

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Non‐Labeled, Stable Labeled, or Radiolabelled Approaches for Provision of Intravenous Pharmacokinetics in Humans: A Discussion Piece

Young,

Spracklin,

James

et al. 2023

Clin Pharma and Therapeutics

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A review of the use of microdoses and isotopic microtracers for clinical intravenous pharmacokinetic (i.v. PK) data provision is presented. The extent of application of the varied approaches available and the relative merits of each are highlighted with the aim of assisting practitioners in making informed decisions on the most scientifically appropriate design to adopt for any given new drug in development. It is envisaged that significant efficiencies will be realized as i.v. PK data in humans becomes more routinely available for suitable assets in early development, than has been the case prior to the last decade.

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Current Approaches for Predicting Human PK for Small Molecule Development Candidates: Findings from the IQ Human PK Prediction Working Group Survey

Cited by 18 publications

References 68 publications

Shared learning from a physiologically based pharmacokinetic modeling strategy for human pharmacokinetics prediction through retrospective analysis of Genentech compounds

Shared learning from a physiologically based pharmacokinetic modeling strategy for human pharmacokinetics prediction through retrospective analysis of Genentech compounds

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)

Non‐Labeled, Stable Labeled, or Radiolabelled Approaches for Provision of Intravenous Pharmacokinetics in Humans: A Discussion Piece

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Current Approaches for Predicting Human PK for Small Molecule Development Candidates: Findings from the IQ Human PK Prediction Working Group Survey

Cited by 18 publications

References 68 publications

Shared learning from a physiologically based pharmacokinetic modeling strategy for human pharmacokinetics prediction through retrospective analysis of Genentech compounds

Shared learning from a physiologically based pharmacokinetic modeling strategy for human pharmacokinetics prediction through retrospective analysis of Genentech compounds

Drug Design and Success of Prospective Mouse In Vitro–In Vivo Extrapolation (IVIVE) for Predictions of Plasma Clearance (CLp) from Hepatocyte Intrinsic Clearance (CLint)

Non‐Labeled, Stable Labeled, or Radiolabelled Approaches for Provision of Intravenous Pharmacokinetics in Humans: A Discussion Piece

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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)