Comprehensive Assessment of Human Pharmacokinetic Prediction Based on In Vivo Animal Pharmacokinetic Data, Part 2: Clearance

Lombardo, Franco; Waters, Nigel J.; Argikar, Upendra A.; Dennehy, Michelle K.; Zhan, Jenny; Gunduz, Mithat; Harriman, Shawn; Berellini, Giuliano; Rajlic, Ivana Liric; Obach, R. Scott

doi:10.1177/0091270012440282

Cited by 89 publications

(76 citation statements)

References 36 publications

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“…6) and the published MLR model (Eq. 7), the values of geometric meanfold error (GMFE) and bias 36 were calculated for both models. These model predictivity metrics are listed in Table 6.…”

Section: Validation On External Data Setsmentioning

confidence: 99%

Survival of the Fittest: Time-To-Event Modeling of Crystallization of Amorphous Poorly Soluble Drugs

Nurzyńska¹,

Austin²,

Fischer

et al. 2016

Journal of Pharmaceutical Sciences

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The objective of this study was to gain a quantitative understanding of the link between physicochemical properties and long-term and time-censored amorphous stability of poorly water-soluble drugs using parametric time-to-event modeling. Previously published data on amorphous stability and physicochemical properties of 25 structurally diverse neutral, poorly soluble compounds were used. To describe the general shape of the survival curve (probability of event at time >t), Constant, Gompertz, and Weibull hazard functions and their linear combinations were tested. For a selected Weibull hazard base model, the effect of each physicochemical covariate was investigated, with combined influence of enthalpy of fusion (Hf) and molecular weight (Mr) showing the highest statistical significance. The covariate model was used to simulate survival curves and calculate the median survival time for different values of Hf and Mr. It was found that a decrease in Hf or an increase in Mr contribute to longer survival times. The derived model equation was validated against external data sets consisting of 11 compounds. It showed better predictive ability than a previously published multiple linear regression model incorporating Hf and Mr. The proposed Weibull covariate model may assist in faster and more cost-effective decision making in the pre-formulation phase of drug development, where compound properties and appropriate drug formulation strategies are investigated.

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“…6) and the published MLR model (Eq. 7), the values of geometric meanfold error (GMFE) and bias 36 were calculated for both models. These model predictivity metrics are listed in Table 6.…”

Section: Validation On External Data Setsmentioning

confidence: 99%

Survival of the Fittest: Time-To-Event Modeling of Crystallization of Amorphous Poorly Soluble Drugs

Nurzyńska¹,

Austin²,

Fischer

et al. 2016

Journal of Pharmaceutical Sciences

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“…Compound selection was based on previous literature studies indicating intestinal metabolism. The human bioavailability databases published by Lombardo et al (2013) , Musther et al (2014) , Bueters et al (2013) and Varma et al (2010) were used for this purpose. An inclusion criterion in this study was the availability of i.v.…”

Section: Methodsmentioning

confidence: 99%

A comparative evaluation of models to predict human intestinal metabolism from nonclinical data

Yau

Petersson

Dolgos

et al. 2017

Biopharm & Drug Disp

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Extensive gut metabolism is often associated with the risk of low and variable bioavailability. The prediction of the fraction of drug escaping gut wall metabolism as well as transporter‐mediated secretion (F g) has been challenged by the lack of appropriate preclinical models. The purpose of this study is to compare the performance of models that are widely employed in the pharmaceutical industry today to estimate F g and, based on the outcome, to provide recommendations for the prediction of human F g during drug discovery and early drug development. The use of in vitro intrinsic clearance from human liver microsomes (HLM) in three mechanistic models – the ADAM, Q gut and Competing Rates – was evaluated for drugs whose metabolism is dominated by CYP450s, assuming that the effect of transporters is negligible. The utility of rat as a model for human F g was also explored. The ADAM, Q gut and Competing Rates models had comparable prediction success (70%, 74%, 69%, respectively) and bias (AFE = 1.26, 0.74 and 0.81, respectively). However, the ADAM model showed better accuracy compared with the Q gut and Competing Rates models (RMSE =0.20 vs 0.30 and 0.25, respectively). Rat is not a good model (prediction success =32%, RMSE =0.48 and AFE = 0.44) as it seems systematically to under‐predict human F g. Hence, we would recommend the use of rat to identify the need for F g assessment, followed by the use of HLM in simple models to predict human F g. © 2017 Merck KGaA. Biopharmaceutics & Drug Disposition Published by John Wiley & Sons, Ltd.

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“…Because other studies have shown that increasing the number of descriptors does not necessarily increase the predictive power from descriptor to PK properties [ 47 , 57 ], and to limit descriptors to those that are easily accessible to the public, no additional descriptors were calculated for this study.…”

Section: Methodsmentioning

confidence: 99%

Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction

et al. 2016

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Developing physiologically-based pharmacokinetic (PBPK) models for chemicals can be resource-intensive, as neither chemical-specific parameters nor in vivo pharmacokinetic data are easily available for model construction. Previously developed, well-parameterized, and thoroughly-vetted models can be a great resource for the construction of models pertaining to new chemicals. A PBPK knowledgebase was compiled and developed from existing PBPK-related articles and used to develop new models. From 2,039 PBPK-related articles published between 1977 and 2013, 307 unique chemicals were identified for use as the basis of our knowledgebase. Keywords related to species, gender, developmental stages, and organs were analyzed from the articles within the PBPK knowledgebase. A correlation matrix of the 307 chemicals in the PBPK knowledgebase was calculated based on pharmacokinetic-relevant molecular descriptors. Chemicals in the PBPK knowledgebase were ranked based on their correlation toward ethylbenzene and gefitinib. Next, multiple chemicals were selected to represent exact matches, close analogues, or non-analogues of the target case study chemicals. Parameters, equations, or experimental data relevant to existing models for these chemicals and their analogues were used to construct new models, and model predictions were compared to observed values. This compiled knowledgebase provides a chemical structure-based approach for identifying PBPK models relevant to other chemical entities. Using suitable correlation metrics, we demonstrated that models of chemical analogues in the PBPK knowledgebase can guide the construction of PBPK models for other chemicals.

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Comprehensive Assessment of Human Pharmacokinetic Prediction Based on In Vivo Animal Pharmacokinetic Data, Part 2: Clearance

Cited by 89 publications

References 36 publications

Survival of the Fittest: Time-To-Event Modeling of Crystallization of Amorphous Poorly Soluble Drugs

Survival of the Fittest: Time-To-Event Modeling of Crystallization of Amorphous Poorly Soluble Drugs

A comparative evaluation of models to predict human intestinal metabolism from nonclinical data

Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction

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