Using mechanistic models to support development of complex generic drug products: European Medicines Agency perspective

Manolis, Efthymios; García‐Arieta, Alfredo; Lindahl, Anders; Kotzagiorgis, Evangelos; Limberg, Jobst; Holte, Øyvind; Paixão, Paulo; Versantvoort, Carolien H.M.; Tshinanu, Flora Musuamba; Blake, Kevin; Heuvel, Michiel van den

doi:10.1002/psp4.12906

Cited by 10 publications

(5 citation statements)

References 5 publications

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“…PBPK model integrates drug-specific data (e.g., blood-to-plasma ratio, PPB, permeability) and physiological parameters (e.g., organ blood flow, organ volume, drug-metabolizing enzyme and transporter abundance) to mechanistically predict the PK profile of a drug (Rowland et al, 2011;Rostami-Hodjegan, 2012). PBPK modeling has a wide range of applications, encompassing PK extrapolation to special populations, prediction of drug-drug interactions, optimization of dosage regimen, and assessment of bioequivalence, among other uses (Zhao et al, 2011;Perry et al, 2020;Abouir et al, 2021;Demeester et al, 2023;Manolis et al, 2023). Regulatory agencies such as the US FDA and EMA have emphasized using PBPK modeling to support regulatory submissions for drug development.…”

Section: Pbpk Modeling To Predict Sirna Disposition During Pregnancymentioning

confidence: 99%

The ADME Characteristics of siRNA Therapeutics and the Opportunity to Predict Disposition in Pregnant Women

Amaeze,

Isoherranen,

Shum

2024

Drug Metabolism and Disposition

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Section: Pbpk Modeling To Predict Sirna Disposition During Pregnancymentioning

confidence: 99%

The ADME Characteristics of siRNA Therapeutics and the Opportunity to Predict Disposition in Pregnant Women

Amaeze,

Isoherranen,

Shum

2024

Drug Metabolism and Disposition

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“…PBPK models, which incorporates anatomical, physiological, and biochemical relationships, are widely used in the drug development process and have proven useful in predicting drug ADME for a range of therapeutics 12,13 , including pediatric drugs 14 , biopharmaceutics 15 , generic drugs 16 , monoclonal antibodies 17 , and nanoparticles 18 . In the case of orally administered drugs, compartmental absorption and transit (CAT) PBPK models are often used.…”

Section: Introductionmentioning

confidence: 99%

Adapting Physiologically-Based Pharmacokinetic Models for Machine Learning Applications

Habiballah

Reisfeld

2023

Preprint

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Both machine learning and physiologically-based pharmacokinetic models are becoming essential components of the drug development process. Integrating the predictive capabilities of physiologically-based pharmacokinetic (PBPK) models within machine learning pipelines could offer significant benefits in improving the accuracy and scope of drug screening and evaluation procedures. Here, we describe the development and testing of a self-contained machine learning module capable of faithfully recapitulating summary pharmacokinetic (PK) parameters produced by a full PBPK model, given a set of input drug-specific and regimen-specific information. Because of its widespread use in characterizing the disposition of orally administered drugs, the PBPK model chosen to demonstrate the methodology was an open-source implementation of a state-of-the-art compartmental and transit model called OpenCAT. The model was tested for drug formulations spanning a large range of solubility and absorption characteristics, and was evaluated for concordance against predictions of OpenCAT and relevant experimental data. In general, the values predicted by the ML models were within 20% of those of the PBPK model across the range of drug and formulation properties. However, summary PK parameter predictions from both the ML model and full PBPK model were occasionally poor with respect to those derived from experiments, suggesting deficiencies in the underlying PBPK model.

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“…Its adoption by the pharmaceutical industry has increased drastically in recent years, evident by a 13-fold increase in the number of PBPK modeling publications supporting US Food and Drug Administration (FDA)-approved drug products from 2012 to 2018 (Perry et al, 2020). Regulatory agencies are also receptive to the use of PBPK models in model-informed drug development (Jean et al, 2021;Luzon et al, 2017;Manolis et al, 2023;X. Zhang et al, 2020); both the FDA (FDA, 2018) and European Medicines Agency (EMA) (EMA, 2018) have issued guidance documents on the proper use of PBPK modeling.…”

Section: Introductionmentioning

confidence: 99%

Potential and Challenges in Application of Physiologically Based Pharmacokinetic Modeling in Predicting Diarrheal Disease Impact on Oral Drug Pharmacokinetics

Zhang,

Arnold

2023

Drug Metab Dispos

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Word count of the introduction: 751 Word count of the minireview body (excluding introduction and conclusion): 8222 Word count of the minireview conclusion: 83 List of Non-standard Abbreviations: A amount of drug A d dissolved mass of drug A u undissolved mass of drug ACAT Advanced Compartment Absorption and Transit ADAM Advanced Dissolution Absorption Metabolism ASF absorption scale factor AUC area under the concentration-time curve BAD bile acid diarrhea BCRP Breast Cancer Resistance Protein BCS biopharmaceutics drug classes C l luminal drug concentration in the gut C max maximum concentration C s solubility of drug

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Using mechanistic models to support development of complex generic drug products: European Medicines Agency perspective

Cited by 10 publications

References 5 publications

The ADME Characteristics of siRNA Therapeutics and the Opportunity to Predict Disposition in Pregnant Women

The ADME Characteristics of siRNA Therapeutics and the Opportunity to Predict Disposition in Pregnant Women

Adapting Physiologically-Based Pharmacokinetic Models for Machine Learning Applications

Potential and Challenges in Application of Physiologically Based Pharmacokinetic Modeling in Predicting Diarrheal Disease Impact on Oral Drug Pharmacokinetics

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