Pharmacokinetics In Drug Discovery Research Articles

Utility of Chimeric Mice with Humanized Liver for Predicting Human Pharmacokinetics in Drug Discovery: Comparison with in Vitro-in Vivo Extrapolation and Allometric Scaling.

Predicting human pharmacokinetics (PK) such as clearance (CL) and volume of distribution (Vd) is a critical component of drug discovery. These predictions are mainly performed by in vitro-in vivo extrapolation (IVIVE) using human biological samples, such as hepatic microsomes and hepatocytes. However, some issues with this process have arisen, such as inconsistencies between in vitro and in vivo findings; the integration of predicted CYP, non-CYP and transporter-mediated human PK; and the difficulty of evaluating very metabolically stable compounds. Various approaches to solving these issues have been reported. Allometric scaling using experimental animals has also often been used. However, this method has also shown many problems due to interspecies differences, albeit that various correction methods have been proposed. Another approach involves the production of chimeric mice with humanized liver via the transplantation of human hepatocytes into mice. The livers of these mice are repopulated mostly with human hepatocytes and express human drug-metabolizing enzymes and drug transporters, suggesting that these mice are useful for solving the issues of IVIVE and allometric scaling, and more reliably predicting human PK. In this review, we summarize human PK prediction methods using IVIVE, allometric scaling and chimeric mice with humanized liver, and discuss the utility of predicting human PK in drug discovery by comparing these chimeric mice with IVIVE and allometric scaling.

Evaluation of Generic Methods to Predict Human Pharmacokinetics Using Physiologically Based PharmacokineticModel for Early Drug Discovery of Tyrosine Kinase Inhibitors.

Requirements for predicting human pharmacokinetics in drug discovery are increasing. Developing different methods of human pharmacokinetic prediction will facilitate lead optimization, candidate nomination, and dosing regimens before clinical trials at various early drug discovery stages. To develop and validate generic methods of human pharmacokinetic prediction to meet the requirements in early drug discovery. The physiologically based pharmacokinetic (PBPK) model implemented in Gastroplus™ was used for human pharmacokinetic predictions. The absorption, distribution, metabolism, and excretion properties of drugs in humans predicted from molecular structure and extrapolated from tested preclinical data were used as inputs in the PBPK model. The approaches were validated by comparison of the predicted pharmacokinetic parameters with actual pharmacokinetic parameters of 15 marketed small-molecule compounds approved by the US Food and Drug Administration. Based on the validation and reported approaches, we proposed a strategy for human pharmacokinetic prediction at different drug discovery stages. Obvious underestimation of exposure (< 1/3 of actual exposure) was not observed using in silico prediction as inputs, which may reduce the probability of missing the potential compounds with predicted false low exposure. The simulated human pharmacokinetic results using tested data as inputs were superior to those obtained via in silico prediction. Both methods similarly predicted the multiphasic shape of pharmacokinetic profiles. These generic PBPK approaches of full in silico prediction or perdition using a combination of tested in vivo and in vitro data were validated and proved useful for human pharmacokinetic predictions.

Contribution of chimeric mice with a humanized liver to the evaluation of pharmacology, toxicity, and pharmacokinetics in drug discovery and development

To develop new drugs with high efficacy and safety, it is important to predict the pharmacological, toxicological, and pharmacokinetic profiles of drug candidates in humans. Chimeric mice with a humanized liver are mice in which human hepatocytes have been transplanted, such that mouse liver cells are replaced with human hepatocytes; these mice have been used as prediction models. Studies performed thus far indicate that chimeric mice with a humanized liver can be used for the prediction of human-specific metabolite formation and plasma concentration-time curves for several drugs. Furthermore, studies advocate the utility of chimeric mice with a humanized liver for modelling drug-induced hepatotoxicity and disease such as hepatitis virus infection in safety and pharmacological evaluations respectively. Taken together, these findings indicate that chimeric mice with a humanized liver can be used to evaluate the relationship between pharmacokinetics, toxicity, and efficacy; the contribution by active metabolites may also be assessed. In recent years, new and improved animal models have been developed to overcome the disadvantages of chimeric mice with a humanized liver. It is expected that their usefulness for optimization of drug candidates and translational research in drug discovery and development will further increase.

Prediction of human pharmacokinetics in drug discovery

Prediction of human pharmacokinetics in drug discovery

Pharmacokinetics in Drug Discovery: An Exposure-Centred Approach to Optimising and Predicting Drug Efficacy and Safety.

The role of pharmacokinetics (PK) in drug discovery is to support the optimisation of the absorption, distribution, metabolism and excretion (ADME) properties of lead compounds with the ultimate goal to attain a clinical candidate which achieves a concentration-time profile in the body that is adequate for the desired efficacy and safety profile. A thorough characterisation of the lead compounds aiming at the identification of the inherent PK liabilities also includes an early generation of PK/PD relationships linking in vitro potency and target exposure/engagement with expression of pharmacological activity (mode-of-action) and efficacy in animal studies. The chapter describes an exposure-centred approach to lead generation, lead optimisation and candidate selection and profiling that focuses on a stepwise generation of an understanding between PK/exposure and PD/efficacy relationships by capturing target exposure or surrogates thereof and cellular mode-of-action readouts in vivo. Once robust PK/PD relationship in animal PD models has been constructed, it is translated to anticipate the pharmacologically active plasma concentrations in patients and the human therapeutic dose and dosing schedule which is also based on the prediction of the PK behaviour in human as described herein. The chapter outlines how the level of confidence in the predictions increases with the level of understanding of both the PK and the PK/PD of the new chemical entities (NCE) in relation to the disease hypothesis and the ability to propose safe and efficacious doses and dosing schedules in responsive patient populations. A sound identification of potential drug metabolism and pharmacokinetics (DMPK)-related development risks allows proposing of an effective de-risking strategy for the progression of the project that is able to reduce uncertainties and to increase the probability of success during preclinical and clinical development.

Quality Control of Natural Product Medicine and Nutrient Supplements

Natural product medicine (NPM) has been used to prevent and treat various human diseases in China, India, Japan, and other countries for centuries. Due to its long historical clinical uses and excellent therapeutic efficacy, NPM more and more attracts global attention, and many research institutes and pharmaceutical companies have been actively exploring NPM as a source for new drug discovery and development [1]. However, the characteristics of NPM are roles of multicomponents and multitargets due to its multiple components. If only few constituents are emphasized, the holistic characteristics of NPM will be ignored [2]. With the development of globalization and modernization of NPM, an important issue is the consistency and control ability of quality of NPM. Traditionally, identification of NPM is performed according to its morphological character, one or a few markers' thin-layer chromatography identification, and/or content determination [3, 4]. However, these methods do not provide a complete profile of the NPM, so it can't distinguish NPM with similar appearance and/or similar main chemical constitutions. The characteristics of action of NPM are synergistic action of multicomponents and multitargets [5]. By a combination of multiple components, NPM may produce its beneficial effects by interacting with different cell signaling pathways and biological networks, achieving the same therapeutic efficacy of a normal isolated compound at much lower doses of isolated compounds [6]. Thus, a comprehensive method which could reflect the variation of most constituents in the NPM is necessary, especially the variation correlating with pharmacological and clinical efficacy. With the development of various modern technologies, advanced chemical, pharmacological, and biological technologies have facilitated an increasing number of researchers in the search for possible ways to explore the potential healthcare benefits of this multicomponents interaction system. In recent years, the analysis of NPM has begun to emphasize more on the integrative and holistic properties of NPM [7]. The chromatographic or spectroscopic fingerprint and multicomponents quantification have been used for the quality control of NPM in the past ten years. Based on some chromatographic or spectroscopic methods, the properties of absorption, distribution, metabolism, and excretion of NPM have been analyzed to screen the bioactive markers. In addition, the genomics and proteomics have been applied to the investigation of NPM. The different analytical techniques and methods of NPM are shown in Table 1. Table 1 The analytical techniques and methods of natural product medicine. In this special issue of quality control of NPM and nutrient supplements, 15 research papers have been published by the previously mentioned chromatographic or spectroscopic methods and contributed to quality control of NPM. These papers focused on the quantification analysis of multiple chemical components, bioactivity and their mechanism of actions of chemical components, and pharmacokinetics and tissue distribution studies. Firstly, multicomponents determinations play a critical role in evaluating the quality of NPM and understanding the synergistic action of multicomponents and multitargets. Multicomponents quantitative methods of NPM were reported in this special issue. Among the analytical methods for NPM, HPLC with easy operation, high accuracy, and wide suitability is still the most popular method for the qualitative and quantitative analyses of NPM. For example, simultaneous quantification of limonin and six alkaloids in Evodia rutaecarpa has been completed by HPLC-DAD method. The results indicated that the quality control of Evodia rutaecarpa could be simplified to the measurement of four constituents and that limonin, 1-methyl-2-undecyl-4(1H)-quinolone, and dihydroevocarpine should also be served as the chemical markers together with evodiamine for the quality control of Evodia rutaecarpa. Secondly, active components of NPM should be clarified by different methods in order to achieve better quality control of NPM and nutrient supplements. The bioactivities of pure compounds or crude extract form NPM have been evaluated by in vitro or in vivo experiments in this special issue. Last but not least, with the development of pharmacy, more and more researchers focus their attention on pharmacokinetics in drug discovery. Pharmacokinetics including absorption, distribution, metabolism, and excretion is regarded as the foundation of new drug discovery. Pharmacokinetics encompasses a broad spectrum of experiment and connotation. Five articles devoted to pharmacokinetics and identification of metabolites from pure compounds or crude extract of NPM. The complexity of single herbs or compound preparations causes diverse pharmacokinetics, and these articles provide key information for researchers in the future studies. All in all, the published research papers will contribute to the development, improvement, validation, and/or extension of application of analytical methodology in the natural medicine sciences and nutrition supplements. Quality control of NPM has to establish reasonable analytical methods for analyzing the active constituents in NPM in order to clarify their therapeutic basis and mechanism of action more clearly. Shuang-Qing Zhang Feng Wei Yu-Ming Fan Feng Sun Ying-Yong Zhao Shuhua Bai

Genetic programming for QSAR investigation of docking energy

Statistical methods, and in particular Machine Learning, have been increasingly used in the drug development workflow to accelerate the discovery phase and to eliminate possible failures early during clinical developments. In the past, the authors of this paper have been working specifically on two problems: (i) prediction of drug induced toxicity and (ii) evaluation of the target–drug chemical interaction based on chemical descriptors. Among the numerous existing Machine Learning methods and their application to drug development (see for instance [F. Yoshida, J.G. Topliss, QSAR model for drug human oral bioavailability, Journal of Medicinal Chemistry 43 (2000) 2575–2585; Frohlich, J. Wegner, F. Sieker, A. Zell, Kernel functions for attributed molecular graphs—a new similarity based approach to ADME prediction in classification and regression, QSAR and Combinatorial Science, 38(4) (2003) 427–431; C.W. Andrews, L. Bennett, L.X. Yu, Predicting human oral bioavailability of a compound: development of a novel quantitative structure–bioavailability relationship, Pharmacological Research 17 (2000) 639–644; J Feng, L. Lurati, H. Ouyang, T. Robinson, Y. Wang, S. Yuan, S.S. Young, Predictive toxicology: benchmarking molecular descriptors and statistical methods, Journal of Chemical Information Computer Science 43 (2003) 1463–1470; T.M. Martin, D.M. Young, Prediction of the acute toxicity (96-h LC50) of organic compounds to the fat head minnow ( Pimephales promelas) using a group contribution method, Chemical Research in Toxicology 14(10) (2001) 1378–1385; G. Colmenarejo, A. Alvarez-Pedraglio, J.L. Lavandera, Chemoinformatic models to predict binding affinities to human serum albumin, Journal of Medicinal Chemistry 44 (2001) 4370–4378; J. Zupan, P. Gasteiger, Neural Networks in Chemistry and Drug Design: An Introduction, 2nd edition, Wiley, 1999]), we have been specifically concerned with Genetic Programming. A first paper [F. Archetti, E. Messina, S. Lanzeni, L. Vanneschi, Genetic programming for computational pharmacokinetics in drug discovery and development, Genetic Programming and Evolvable Machines 8(4) (2007) 17–26] has been devoted to problem (i). The present contribution aims at developing a Genetic Programming based framework on which to build specific strategies which are then shown to be a valuable tool for problem (ii). In this paper, we use target estrogen receptor molecules and genistein based drug compounds. Being able to precisely and efficiently predict their mutual interaction energy is a very important task: for example, it may have an immediate relationship with the efficacy of genistein based drugs in menopause therapy and also as a natural prevention of some tumors. We compare the experimental results obtained by Genetic Programming with the ones of a set of “non-evolutionary” Machine Learning methods, including Support Vector Machines, Artificial Neural Networks, Linear and Least Square Regression. Experimental results confirm that Genetic Programming is a promising technique from the viewpoint of the accuracy of the proposed solutions, of the generalization ability and of the correlation between predicted data and correct ones.

N-in-1 Dosing Pharmacokinetics in Drug Discovery: Experience, Theoretical and Practical Considerations

N-in-1 (or cassette) dosing pharmacokinetics (PK) has been used in drug discovery for rapid assessment of PK properties of new chemical entities. However, because of potential for drug–drug interactions this procedure is still controversial. This study was to retrospectively evaluate the N-in-1 dosing approach in drug discovery with an emphasis on the potential for drug–drug interactions. The systemic clearance, volume of distribution, oral bioavailability, and renal excretion of the 31 lead compounds in rats, dogs or chimpanzees were significantly correlated between the N-in-1 dosing and discrete studies with r values of 0.69, 0.91, 0.53, and 0.83 (p<0.005 for all), respectively. PK parameters for 11 quality control compounds which were involved in 194 N-in-1 studies for screening approximately 1000 compounds had coefficient of variations of less than 70%. The intrinsic microsomal clearances generated from the N-in-1 and discrete incubations were nearly identical (r=0.97, p<0.0001). The intrinsic clearances of quality control compound from the N-in-1 incubations were consistent with its discrete CLint estimate (cv: 5.4%). Therefore, N-in-1 dosing is a useful approach in drug discovery to quickly obtain initial PK estimates. Potential drug–drug interactions that result in confounding PK estimates do not occur as frequently as expected. © 2007 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 97:2568–2580, 2008

Genetic programming for computational pharmacokinetics in drug discovery and development

The success of a drug treatment is strongly correlated with the ability of a molecule to reach its target in the patient's organism without inducing toxic effects. Moreover the reduction of cost and time associated with drug discovery and development is becoming a crucial requirement for pharmaceutical industry. Therefore computational methods allowing reliable predictions of newly synthesized compounds properties are of outmost relevance. In this paper we discuss the role of genetic programming in predictive pharmacokinetics, considering the estimation of adsorption, distribution, metabolism, excretion and toxicity processes (ADMET) that a drug undergoes into the patient's organism. We compare genetic programming with other well known machine learning techniques according to their ability to predict oral bioavailability (%F), median oral lethal dose (LD50) and plasma-protein binding levels (%PPB). Since these parameters respectively characterize the percentage of initial drug dose that effectively reaches the systemic blood circulation, the harmful effects and the distribution into the organism of a drug, they are essential for the selection of potentially good molecules. Our results suggest that genetic programming is a valuable technique for predicting pharmacokinetics parameters, both from the point of view of the accuracy and of the generalization ability.

Application of substrate depletion assay for early prediction of nonlinear pharmacokinetics in drug discovery: Assessment of nonlinearity of metoprolol, timolol, and propranolol

The purpose of this study was to investigate the advantages of the substrate depletion assay for evaluating linearity of pharmacokinetics compared with the metabolite formation assay. For propranolol, metoprolol, and nisoldipine with multiple and/or sequential metabolisms, the Michaelis constant (Km) and maximum metabolic intrinsic clearance obtained from the depletion assay using rat and human liver microsomes showed a good correlation with relevant parameters with the formation assay. In vitro kinetics and in vivo pharmacokinetic profiles after oral administration of timolol, metoprolol, and propranolol, were investigated in rats using the depletion assay. The same rank order was found between nonlinearities based on dose-normalized areas under the plasma concentration curve (AUC/Dose) and Km values. Using the kinetic parameters of these compounds, AUC was predicted based on a physiological based pharmacokinetic model incorporated saturable metabolism. The AUCs predicted for propranolol and metoprolol had a good relationship with those observed in the in vivo studies, implying that the depletion assay could be useful for assessing linearity of pharmacokinetics.

Book Review: Pharmacokinetics in Drug Discovery and Development

Book Review: Pharmacokinetics in Drug Discovery and Development