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ology; (b) there are apical (AT) and basolateral (BT) uptake transporters for P-gp substrates in most, if not all, P-gp expressing cell lines used in the pharmaceutical industry, which exist, but which remain unidentified; (c) the lab-to-lab variability in P-gp IC50 values observed in the P-gp IC50 initiative was due to the conflated inhibition of P-gp and the basolateral digoxin uptake transporters by all 15 P-gp substrates tested in that study; (d) even the IC50 values for P-gp inhibition alone do not obey the Cheng-Prusoff relationship; (e) the fitted elementary rate constants and the molecular dissociation constant Ki for this kinetic model are system independent; and (f) the time dependence of product formation for these confluent cell monolayers is correlated with the P-gp Vmax/Km, when defined by its fitted elementary rate constants and uptake transporter clearances, without any steady-state assumptions.Assessing the interactions of a new drug candidate with transporters, either as a substrate, inhibitor, or inducer, is no simple matter. There are many clinically relevant transporters, as many as nine to be evaluated for an FDA submission and up to 11 for the EMA as of 2020. Additionally, it is likely that if a compound is a substrate or inhibitor of one transporter, it will be so for other transporters as well. There are practically no specific substrates or inhibitors, presumably because the specificities of drug transporters are so broad and overlapping, and even fewer clinically relevant probes that can be used to evaluate transporter function in humans. In the case of some transporters, it is advisable to evaluate an NCE with more than one test system and/or more than one probe substrate in order to convince oneself (and regulatory authorities) that a clinical drug interaction study is not warranted. Finally, each test system has its own unique set of advantages and disadvantages. One has to appreciate the nuances of the available tools (test systems, probe substrates, etc.) to select the most relevant tools for the study and design the optimal in vitro experiment. In this chapter, several examples are used to illustrate the successful interpretation of in vitro data for both efflux and uptake transporters. Some data presented in this chapter are unpublished at the time of the compilation of this book. It has been included in this chapter to provide a sense of the complexities in transporter kinetics to the reader.New molecular entities (NMEs) are evaluated using a rigorous set of in vitro and in vivo studies to assess their safety and suitability for testing in humans. Navitoclax solubility dmso Regulatory health authorities require that therapeutic and supratherapeutic doses be administered, by the intended route of administration, to two nonclinical species prior to human testing. The purpose of these studies is to identify potential target organ toxicity and to determine if the effects are reversible. Liver is a potential site for toxicity caused by orally administered NMEs due to high exposure during first pass after oral administration. A range of clinical chemistry analytes are routinely measured in both nonclinical and clinical studies to evaluate and monitor for hepatotoxicity. While bilirubin itself circulates within a wide range of concentrations in many animal species and humans, without causing adverse effects and possibly providing benefits, bilirubin is one of the few readily monitored circulating biomarkers that can provide insigtudies needed for risk assessment and for identifying the mechanisms of bilirubin elevation. Caveats of methods and data interpretation are discussed in these case studies. The data presented in this chapter is unpublished at the time of compilation of this book. It has been incorporated in this chapter to provide a sense of complexities in enzyme kinetics to the reader.Predicting drug-drug interactions (DDIs) from in vitro data is made difficult by not knowing concentrations of substrate and inhibitor at the target site. For in vivo targets, this is understandable, since intracellular concentrations can differ from extracellular concentrations. More vexing is that the concentration of the drug at the target for some in vitro assays can also be unknown. This uncertainty has resulted in standard in vitro practices that cannot accurately predict human pharmacokinetics. This case study highlights the impact of drug distribution, both in vitro and in vivo, with the example of the drug interaction potential of montelukast.An appreciation of enzyme kinetic principles can be applied in a number of drug metabolism applications. The concept for this chapter arose from a simple discussion on selecting appropriate time points to most efficiently assess metabolite profiles in a human Phase 1a clinical study (Subheading 4). By considering enzyme kinetics, a logical approach to the issue was derived. The dialog was an important learning opportunity for the participants in the discussion, and we have endeavored to capture this experience with other questions related to determination of Km and Vmax parameters, a consideration of the value of hepatocytes vs. liver microsomes, and enzyme inhibition parameters.In this chapter, we illustrate the criticality of proper fitting of enzyme kinetic data. Simple techniques are provided to arrive at meaningful kinetic parameters, illustrated using an example, nonmonotonic data set. In the initial analysis of this data set, derived Km and Vmax parameters incorporated into PBPK models resulted in outcomes that did not adequately describe clinical data. This prompted a re-review of the in vitro data set and curve-fitting procedures. During this review, it was found that the 3-parameter model was fitted on data that was improperly unweighted. Reanalysis of the data using a weighted model returned a better fit and resulted in kinetic parameters better aligning with clinical data. Tools and techniques used to identify and compare kinetic models of this data set are provided, including various replots, visual inspection, examination of residuals, and the Akaike information criterion.Characterization of enzyme kinetics in an experiment is dependent on measurement of a change in concentration of either the substrate (loss of parent) or the product (formation of metabolite). Modern analytical techniques such as ultrahigh pressure liquid chromatography, high resolution mass spectrometry, etc. have allowed accurate characterization of minute changes in concentration. Therefore, complex kinetic data such as a sigmoidal phase at low substrate concentrations or terminal half-life in a PK curve can be evaluated by stretching the limits of analytical quantification. This chapter presents some elementary dos and don'ts and provides insight into some of the underlying principles for utilizing the best possible analytical techniques when investigating enzyme kinetics. The objective of this case study is to answer the following questions (a) Why is it necessary to determine lower and upper limits of quantification (LLOQ and ULOQ, respectively) of a bioanalytical assay, specifically for enzyme kinetic assays? How do you utilize LLOQ and ULOQ to correctly interpret your kinetic data? (b) Why should one use a linear fit and not a quadratic fit for standard curves? (c) Is quantification of an analyte possible without a reference standard? Can one assume equal signal intensities regardless of analytical technique (MS, UV)? (d) In the absence of reference standards, can you still determine kinetic constants? (e) With the need to keep substrate depletion at less than 20% for linearity assumptions, does bioanalytical variability matter? (f) What buffer do you use for your enzyme systems? How do you choose your buffer ? Does choice of bioanalytical methods (LC, MS) dictate your choice of buffer ?