Dataset Information

Prediction of Metabolite-to-Parent Drug Exposure: Derivation and Application of a Mechanistic Static Model.

ABSTRACT: In the development of new drugs, the prediction of metabolite-to-parent plasma exposure ratio in humans prior to administration in a clinical study has emerged as an important need. In this work, we derived a mechanistic static model based on first principles to estimate metabolite-to-parent plasma exposure ratio, considering the contribution of liver and gut metabolism and drug transport. Knowledge (or assumptions) of mechanisms of clearance and organs involved is required. Input parameters needed included intrinsic clearance, fraction of clearance to the metabolite of interest, various binding values, and, in some cases, active transport clearance. The principles are illustrated with four drugs that yield six metabolites, with one in which clearance is dependent on a pathway subject to genetic polymorphism. Overall, the approach yielded metabolite-to-parent ratios within about twofold of the actual values and, thus, can be valuable in decision making in the drug development process.

SUBMITTER: Callegari E

PROVIDER: S-EPMC7214656 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Prediction of Metabolite-to-Parent Drug Exposure: Derivation and Application of a Mechanistic Static Model.

Callegari Ernesto E Varma Manthena V S MVS Obach R Scott RS

Clinical and translational science 20200204 3

In the development of new drugs, the prediction of metabolite-to-parent plasma exposure ratio in humans prior to administration in a clinical study has emerged as an important need. In this work, we derived a mechanistic static model based on first principles to estimate metabolite-to-parent plasma exposure ratio, considering the contribution of liver and gut metabolism and drug transport. Knowledge (or assumptions) of mechanisms of clearance and organs involved is required. Input parameters nee ...[more]

PMID: 31880865

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Project description:Background and objectiveMechanistic static and dynamic physiologically based pharmacokinetic models are used in clinical drug development to assess the risk of drug-drug interactions (DDIs). Currently, the use of mechanistic static models is restricted to screening DDI risk for an investigational drug, while dynamic physiologically based pharmacokinetic models are used for quantitative predictions of DDIs to support regulatory filing. As physiologically based pharmacokinetic model development by sponsors as well as a review of models by regulators require considerable resources, we explored the possibility of using mechanistic static models to support regulatory filing, using representative cases of successful physiologically based pharmacokinetic submissions to the US Food and Drug Administration under different classes of applications.MethodsDrug-drug interaction predictions with mechanistic static models were done for representative cases in the different classes of applications using the same data and modelling workflow as described in the Food and Drug Administration clinical pharmacology reviews. We investigated the hypothesis that the use of unbound average steady-state concentrations of modulators as driver concentrations in the mechanistic static models should lead to the same conclusions as those from physiologically based pharmacokinetic modelling for non-dynamic measures of DDI risk assessment such as the area under the plasma concentration-time curve ratio, provided the same input data are employed for the interacting drugs.ResultsDrug-drug interaction predictions of area under the plasma concentration-time curve ratios using mechanistic static models were mostly comparable to those reported in the Food and Drug Administration reviews using physiologically based pharmacokinetic models for all representative cases in the different classes of applications.ConclusionsThe results reported in this study should encourage the use of models that best fit an intended purpose, limiting the use of physiologically based pharmacokinetic models to those applications that leverage its unique strengths, such as what-if scenario testing to understand the effect of dose staggering, evaluating the role of uptake and efflux transporters, extrapolating DDI effects from studied to unstudied populations, or assessing the impact of DDIs on the exposure of a victim drug with concurrent mechanisms. With this first step, we hope to trigger a scientific discussion on the value of a routine comparison of the two methods for regulatory submissions to potentially create a best practice that could help identify examples where the use of dynamic changes in modulator concentrations could make a difference to DDI risk assessment.

Project description:MotivationA key challenge in metabolomics is annotating measured spectra from a biological sample with chemical identities. Currently, only a small fraction of measurements can be assigned identities. Two complementary computational approaches have emerged to address the annotation problem: mapping candidate molecules to spectra, and mapping query spectra to molecular candidates. In essence, the candidate molecule with the spectrum that best explains the query spectrum is recommended as the target molecule. Despite candidate ranking being fundamental in both approaches, limited prior works incorporated rank learning tasks in determining the target molecule.ResultsWe propose a novel machine learning model, Ensemble Spectral Prediction (ESP), for metabolite annotation. ESP takes advantage of prior neural network-based annotation models that utilize multilayer perceptron (MLP) networks and Graph Neural Networks (GNNs). Based on the ranking results of the MLP- and GNN-based models, ESP learns a weighting for the outputs of MLP and GNN spectral predictors to generate a spectral prediction for a query molecule. Importantly, training data is stratified by molecular formula to provide candidate sets during model training. Further, baseline MLP and GNN models are enhanced by considering peak dependencies through label mixing and multi-tasking on spectral topic distributions. When trained on the NIST 2020 dataset and evaluated on the relevant candidate sets from PubChem, ESP improves average rank by 23.7% and 37.2% over the MLP and GNN baselines, respectively, demonstrating performance gain over state-of-the-art neural network approaches. However, MLP approaches remain strong contenders when considering top five ranks. Importantly, we show that annotation performance is dependent on the training dataset, the number of molecules in the candidate set and candidate similarity to the target molecule.Availability and implementationThe ESP code, a trained model, and a Jupyter notebook that guide users on using the ESP tool is available at https://github.com/HassounLab/ESP.

Dataset Information

Prediction of Metabolite-to-Parent Drug Exposure: Derivation and Application of a Mechanistic Static Model.

Publications

Prediction of Metabolite-to-Parent Drug Exposure: Derivation and Application of a Mechanistic Static Model.

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