A multi‐model approach to predict efficacious clinical dose for an <scp>anti‐TGF</scp>‐β antibody (<scp>GC2008</scp>) in the treatment of osteogenesis imperfecta

Mavroudis, Panteleimon D.; Pillai, Nikhil; Wang, Qingping; Pouzin, Clemence; Greene, Benjamin; Fretland, Jennifer

doi:10.1002/psp4.12857

Cited by 3 publications

(3 citation statements)

References 51 publications

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“…Despite this inherent limitation, the 1-compartment model was able to capture the majority of both AUC last, and C max of observed data within a two-/three-fold difference (Figures 3B, C). In pharmacokinetics/toxicokinetics, there is no a priori threshold on an acceptable model error, but PBPK/PK models are generally accepted and considered useful when the prediction error on AUC or Cmax is in the two-or three-fold range (De Buck et al, 2007;Maharaj and Edginton, 2014;Mavroudis et al, 2018;Mavroudis et al, 2022). To predict a new molecule's tissue distribution in the absence of exposure data, quantitative pharmacologists often use PBPK modeling (Chen et al, 2012;Jones et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Application of machine learning in combination with mechanistic modeling to predict plasma exposure of small molecules

et al. 2023

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Prediction of a new molecule’s exposure in plasma is a critical first step toward understanding its efficacy/toxicity profile and concluding whether it is a possible first-in-class, best-in-class candidate. For this prediction, traditional pharmacometrics use a variety of scaling methods that are heavily based on pre-clinical pharmacokinetic (PK) data. We here propose a novel framework based on which preclinical exposure prediction is performed by applying machine learning (ML) in tandem with mechanism-based modeling. In our proposed method, a relationship is initially established between molecular structure and physicochemical (PC)/PK properties using ML, and then the ML-driven PC/PK parameters are used as input to mechanistic models that ultimately predict the plasma exposure of new candidates. To understand the feasibility of our proposed framework, we evaluated a number of mechanistic models (1-compartment, physiologically based pharmacokinetic (PBPK)), PBPK distribution models (Berezhkovskiy, PK-Sim standard, Poulin and Theil, Rodgers and Rowland, and Schmidt), and PBPK parameterizations (using in vivo, or in vitro clearance). For most of the scenarios tested, our results demonstrate that PK profiles can be adequately predicted based on the proposed framework. Our analysis further indicates some limitations when liver microsomal intrinsic clearance (CLint) is used as the only clearance pathway and underscores the necessity of investigating the variability emanating from the different distribution models when providing PK predictions. The suggested approach aims at earlier exposure prediction in the drug development process so that critical decisions on molecule screening, chemistry design, or dose selection can be made as early as possible.

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Section: Discussionmentioning

confidence: 99%

Application of machine learning in combination with mechanistic modeling to predict plasma exposure of small molecules

et al. 2023

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“…PK evaluation plays a critical role in understanding a compound's safety and efficacy and determining its starting and efficacious dose for future clinical studies 2–4 . Traditionally, PK in humans is predicted by scaling PK from preclinical species (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…PK evaluation plays a critical role in understanding a compound's safety and efficacy and determining its starting and efficacious dose for future clinical studies. 2 , 3 , 4 Traditionally, PK in humans is predicted by scaling PK from preclinical species (e.g. mice, rats, dogs, mini‐pigs and non‐human primates (NHP)), a method that requires extensive use of animal experiments.…”

Section: Introductionmentioning

confidence: 99%

Machine learning framework to predict pharmacokinetic profile of small molecule drugs based on chemical structure

Pillai,

Abos,

Teutonico

et al. 2024

Clinical Translational Sci

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Accurate prediction of a new compound's pharmacokinetic (PK) profile is pivotal for the success of drug discovery programs. An initial assessment of PK in preclinical species and humans is typically performed through allometric scaling and mathematical modeling. These methods use parameters estimated from in vitro or in vivo experiments, which although helpful for an initial estimation, require extensive animal experiments. Furthermore, mathematical models are limited by the mechanistic underpinning of the drugs' absorption, distribution, metabolism, and elimination (ADME) which are largely unknown in the early stages of drug discovery. In this work, we propose a novel methodology in which concentration versus time profile of small molecules in rats is directly predicted by machine learning (ML) using structure‐driven molecular properties as input and thus mitigating the need for animal experimentation. The proposed framework initially predicts ADME properties based on molecular structure and then uses them as input to a ML model to predict the PK profile. For the compounds tested, our results demonstrate that PK profiles can be adequately predicted using the proposed algorithm, especially for compounds with Tanimoto score greater than 0.5, the average mean absolute percentage error between predicted PK profile and observed PK profile data was found to be less than 150%. The suggested framework aims to facilitate PK predictions and thus support molecular screening and design earlier in the drug discovery process.

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A multi‐model approach to predict efficacious clinical dose for an anti‐TGF‐β antibody (GC2008) in the treatment of osteogenesis imperfecta

Mavroudis

Pillai

Wang

et al. 2022

CPT Pharmacom & Syst Pharma

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Osteogenesis imperfecta (OI) is a heterogeneous group of inherited bone dysplasias characterized by reduced skeletal mass and bone fragility. Although the primary manifestation of the disease involves the skeleton, OI is a generalized connective tissue disorder that requires a multidisciplinary treatment ap- Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?Efficacious human dose prediction is a complex process as compound's biopharmaceutical, pharmacokinetic (PK), and pharmacodynamic (PD) properties informed from preclinical species must be projected to humans. The current knowledge gap was translating pharmacology for patients with osteogenesis imperfecta (OI) because there is not current TGF-β directed treatment. WHAT QUESTION DID THIS STUDY ADDRESS? Evaluation of efficacious dose for a new anti-TGF-β antibody drug candidate (GC2008), for the treatment of OI. WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?In this study, we propose the use of a multi-model approach to inform on projected human doses. The tiered modeling approach shown in this work is based on preclinical and sparse clinical PK/PD data in which targeting TGF-β was investigated for the treatment of OI. HOW MIGHT THIS CHANGE DRUG DISCOVERY, DEVELOPMENT, AND/OR THERAPEUTICS?Use of a tiered multi-model approach incorporating mathematical models that evaluate different aspects of disease, can be sued to build confidence in translation projected pharmacology for the treatment of OI.

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A multi‐model approach to predict efficacious clinical dose for an anti‐TGF‐β antibody (GC2008) in the treatment of osteogenesis imperfecta

Cited by 3 publications

References 51 publications

Application of machine learning in combination with mechanistic modeling to predict plasma exposure of small molecules

Application of machine learning in combination with mechanistic modeling to predict plasma exposure of small molecules

Machine learning framework to predict pharmacokinetic profile of small molecule drugs based on chemical structure

A multi‐model approach to predict efficacious clinical dose for an anti‐TGF‐β antibody (GC2008) in the treatment of osteogenesis imperfecta

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