Quantitative Measurement of Rate of Targeted Protein Degradation

Targeted protein degradation (TPD) is a therapeutic approach that leverages the cell's natural machinery to degrade targets instead of inhibiting them. This is accomplished by using mono- or bifunctional small molecules designed to induce the proximity of target proteins and E3 ubiquitin ligases, leading to ubiquitination and subsequent proteasome-dependent degradation of the target. One of the most significant attributes of the TPD approach is its proposed catalytic mechanism of action, which permits substoichiometric exposure to achieve the desired pharmacological effects. However, apart from one in vitro study, studies supporting the catalytic mechanism of degraders are largely inferred based on potency. A more comprehensive understanding of the degrader catalytic mechanism of action can help aspects of compound development. To address this knowledge gap, we developed a workflow for the quantitative measurement of the catalytic rate of degraders in cells. Comparing a selective and promiscuous BTK degrader, we demonstrate that both compounds function as efficient catalysts of BTK degradation, with the promiscuous degrader exhibiting faster rates due to its ability to induce more favorable ternary complexes. By leveraging computational modeling, we show that the catalytic rate is highly dynamic as the target is depleted from cells. Further investigation of the promiscuous kinase degrader revealed that the catalytic rate is a better predictor of optimal degrader activity toward a specific target compared to degradation magnitude alone. In summary, we present a versatile method for mapping the catalytic activity of any degrader for TPD in cells.

Heterogeneous brain distribution of bumetanide following systemic administration in rats

Bumetanide is used widely as a tool and off-label treatment to inhibit the Na-K-2Cl cotransporter NKCC1 in the brain and thereby to normalize intra-neuronal chloride levels in several brain disorders. However, following systemic administration, bumetanide only poorly penetrates into the brain parenchyma and does not reach levels sufficient to inhibit NKCC1. The low brain penetration is a consequence of both the high ionization rate and plasma protein binding, which restrict brain entry by passive diffusion, and of brain efflux transport. In previous studies, bumetanide was determined in the whole brain or a few brain regions, such as the hippocampus. However, the blood-brain barrier and its efflux transporters are heterogeneous across brain regions, so it cannot be excluded that bumetanide reaches sufficiently high brain levels for NKCC1 inhibition in some discrete brain areas. Here, bumetanide was determined in 14 brain regions following i.v. administration of 10 mg/kg in rats. Because bumetanide is much more rapidly eliminated by rats than humans, its metabolism was reduced by pretreatment with piperonyl butoxide. Significant, up to 5-fold differences in regional bumetanide levels were determined with the highest levels in the midbrain and olfactory bulb and the lowest levels in the striatum and amygdala. Brain:plasma ratios ranged between 0.004 (amygdala) and 0.022 (olfactory bulb). Regional brain levels were significantly correlated with local cerebral blood flow. However, regional bumetanide levels were far below the IC50 (2.4 μM) determined previously for rat NKCC1. Thus, these data further substantiate that the reported effects of bumetanide in rodent models of brain disorders are not related to NKCC1 inhibition in the brain.

Non-specific binding of compounds in in vitro metabolism assays: a comparison of microsomal and hepatocyte binding in different species and an assessment of the accuracy of prediction models

Non-specific binding in in vitro metabolism systems leads to an underestimation of the true intrinsic metabolic clearance of compounds being studied. Therefore in vitro binding needs to be accounted for when extrapolating in vitro data to predict the in vivo metabolic clearance of a compound. While techniques exist for experimentally determining the fraction of a compound unbound in in vitro metabolism systems, early in drug discovery programmes computational approaches are often used to estimate the binding in the in vitro system.Experimental fraction unbound data (n = 60) were generated in liver microsomes (fu_mic) from five commonly used pre-clinical species (rat, mouse, dog, minipig, monkey) and humans. Unbound fraction in incubations with mouse, rat or human hepatocytes was determined for the same 60 compounds. These data were analysed to determine the relationship between experimentally determined binding in the different matrices and across different species. In hepatocytes there was a good correlation between fraction unbound in human and rat (r²=0.86) or mouse (r²=0.82) hepatocytes. Similar correlations were observed between binding in human liver microsomes and microsomes from rat, mouse, dog, Göttingen minipig or monkey liver microsomes (r² of >0.89, n = 51 - 52 measurements in different species). Physicochemical parameters (logP, pKa and logD) were predicted for all evaluated compounds. In addition, logP and/or logD were measured for a subset of compounds.Binding to human hepatocytes predicted using 5 different methods was compared to the measured data for a set of 59 compounds. The best methods evaluated used measured microsomal binding in human liver microsomes to predict hepatocyte binding. The collated physicochemical data were used to predict the human fu_mic using four different in silico models for a set of 53-60 compounds. The correlation (r²) and root mean square error between predicted and observed microsomal binding was 0.69 & 0.20, 0.47 & 0.23, 0.56 & 0.21 and 0.54 & 0.26 for the Turner-Simcyp, Austin, Hallifax-Houston and Poulin models, respectively. These analyses were extended to include measured literature values for binding in human liver microsomes for a larger set of compounds (n=697). For the larger dataset of compounds, microsomal binding was well predicted for neutral compounds (r²=0.67 - 0.70) using the Poulin, Austin, or Turner-Simcyp methods but not for acidic or basic compounds (r²<0.5) using any of the models. While the lipophilicity-based models can be used, the in vitro binding should be measured for compounds where more certainty is needed, using appropriately calibrated assays and possibly established weak, moderate, and strong binders as reference compounds to allow comparison across databases.

A Permeability- and Perfusion-based PBPK model for Improved Prediction of Concentration-time Profiles

To improve predictions of concentration‐time (C‐t) profiles of drugs, a new physiologically based pharmacokinetic modeling framework (termed ‘PermQ’) has been developed. This model includes permeability into and out of capillaries, cell membranes, and intracellular lipids. New modeling components include (i) lumping of tissues into compartments based on both blood flow and capillary permeability, and (ii) parameterizing clearances in and out of membranes with apparent permeability and membrane partitioning values. Novel observations include the need for a shallow distribution compartment particularly for bases. C‐t profiles were modeled for 24 drugs (7 acidic, 5 neutral, and 12 basic) using the same experimental inputs for three different models: Rodgers and Rowland (RR), a perfusion‐limited membrane‐based model (K_p,mem), and PermQ. K_p,mem and PermQ can be directly compared since both models have identical tissue partition coefficient parameters. For the 24 molecules used for model development, errors in V_ss and t_1/2 were reduced by 37% and 43%, respectively, with the PermQ model. Errors in C‐t profiles were reduced (increased EOC) by 43%. The improvement was generally greater for bases than for acids and neutrals. Predictions were improved for all 3 models with the use of parameters optimized for the PermQ model. For five drugs in a test set, similar results were observed. These results suggest that prediction of C‐t profiles can be improved by including capillary and cellular permeability components for all tissues.

Using Partition Analysis as a Facile Method to Derive Net Clearances

Partition analysis has been described previously by W.W. Cleland to derive net rate constants and simplify the derivation of enzyme kinetic equations. Here, we show that partition analysis can be used to derive elimination and transfer (distribution) net clearances for use in pharmacokinetic models. For elimination clearances, the net clearance approach is exemplified with a mammillary two‐compartment model with peripheral elimination, and the established well‐stirred and full hepatic clearance models. The intrinsic hepatic clearance associated with an observed average hepatic clearance can be easily calculated with net clearances. Expressions for net transfer clearances are easily derived, including models with explicit membranes (e.g., monolayer permeability and blood–brain barrier models). Together, these approaches can be used to derive equations for physiologically based and hybrid compartmental/ physiologically based models. This tutorial describes how net clearances can be used to derive relationships for simple models as well as increasingly complex models, such as inclusion of active transport and target mediated processes.

Numerical Methods for Modeling Enzyme Kinetics

Differential equations are used to describe time-dependent changes in enzyme kinetics and pharmacokinetics. Analytical and numerical methods can be used to solve differential equations. This chapter describes the use of numerical methods in solving differential equations and its applications in characterizing the complexities observed in enzyme kinetics. A discussion is included on the use of numerical methods to overcome limitations of explicit equations in the analysis of metabolism kinetics, reversible inhibition kinetics, and inactivation kinetics. The chapter describes the advantages of using numerical methods when Michaelis-Menten assumptions do not hold.

Ligand binding at the protein-lipid interface: strategic considerations for drug design

Many drug targets are embedded within the phospholipid bilayer of cellular membranes, including G protein-coupled receptors, ion channels, transporters and membrane-bound enzymes. Increasing evidence from biophysical and structural studies suggests that many small-molecule drugs commonly associate with these targets at binding sites at the protein-phospholipid interface. Without a direct path from bulk solvent to a binding site, a drug must first partition in the phospholipid membrane before interacting with the protein target. This membrane access mechanism necessarily affects the interpretation of potency data, structure-activity relationships, pharmacokinetics and physicochemical properties for drugs that target these sites. With an increasing number of small-molecule intramembrane binding sites revealed through X-ray crystallography and cryogenic electron microscopy, we suggest that ligand-lipid interactions likely play a larger role in small-molecule drug action than commonly appreciated. This Perspective introduces key concepts and drug design considerations to aid discovery teams operating within this target space, and discusses challenges and future opportunities in the field.

Case Study 5: Predicting the Drug Interaction Potential for Inhibition of CYP2C8 by Montelukast

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.

Consideration of the Unbound Drug Concentration in Enzyme Kinetics

The study of enzyme kinetics in drug metabolism involves assessment of rates of metabolism and inhibitory potencies over a suitable concentration range. In all but the very simplest in vitro system, these drug concentrations can be influenced by a variety of nonspecific binding reservoirs that can reduce the available concentration to the enzyme system(s) under investigation. As a consequence, the apparent kinetic parameters, such as K_m or K_i, that are derived can deviate from the true values. There are a number of sources of these nonspecific binding depots or barriers, including membrane permeation and partitioning, plasma or serum protein binding, and incubational binding. In the latter case, this includes binding to the assay apparatus as well as biological depots, depending on the characteristics of the in vitro matrix being used. Given the wide array of subcellular, cellular, and recombinant enzyme systems utilized in drug metabolism, each of these has different components which can influence the free drug concentration. The physicochemical properties of the test compound are also paramount in determining the influential factors in any deviation between true and apparent kinetic behavior. This chapter describes the underlying mechanisms determining the free drug concentration in vitro and how these factors can be accounted for in drug metabolism studies, illustrated with case studies from the literature.

Radiosynthesis and evaluation of 18F-labeled dopamine D4-receptor ligands

INTRODUCTION

The dopamine D4 receptor (D4R) has attracted considerable attention as potential target for the treatment of a broad range of central nervous system disorders. Although many efforts have been made to improve the performance of putative radioligand candidates, there is still a lack of D4R selective tracers suitable for in vivo PET imaging. Thus, the objective of this work was to develop a D4-selective PET ligand for clinical applications.

METHODS

Four compounds based on previous and new lead structures were prepared and characterized with regard to their D4R subtype selectivity and predicted lipophilicity. From these, 3-((4-(2-fluorophenyl)piperazin-1-yl)methyl)-1H-pyrrolo[2,3-b]pyridine I and (S)-4-(3-fluoro-4-methoxybenzyl)-2-(phenoxymethyl)morpholine II were selected for labeling with fluorine-18 and subsequent evaluation by in vitro autoradiography to assess their suitability as D4 radioligand candidates for in vivo imaging.

RESULTS

The radiosynthesis of [18F]I and [18F]II was successfully achieved by copper-mediated radiofluorination with radiochemical yields of 7% and 66%, respectively. The radioligand [18F]II showed specific binding in areas where D4 expression is expected, whereas [18F]I did not show any uptake in distinct brain regions and exhibited an unacceptable degree of non-specific binding.

CONCLUSIONS

The compounds studied exhibited high D4R subtype selectivity and logP values compatible with high brain uptake, but only ligand [18F]II showed low non-specific binding and is therefore a good candidate for further evaluation.

ADVANCES IN KNOWLEDGE

The discovery of new lead structures for high-affinity D4 ligands opens up new possibilities for the development of suitable PET-radioligands.

IMPLICATIONS FOR PATIENT

PET-imaging of dopamine D4-receptors could facilitate understanding, diagnosis and treatment of neuropsychiatric and neurodegenerative diseases.

Enzyme Kinetics of Uridine Diphosphate Glucuronosyltransferases (UGTs)

Glucuronidation, catalyzed by uridine diphosphate glucuronosyltransferases (UGTs), is an important process for the metabolism and clearance of many lipophilic chemicals, including drugs, environmental chemicals, and endogenous compounds. Glucuronidation is a bisubstrate reaction that requires the aglycone and the cofactor, UDP-GlcUA. Accumulating evidence suggests that the bisubstrate reaction follows a compulsory-order ternary mechanism. To simplify the kinetic modeling of glucuronidation reactions in vitro, UDP-GlcUA is usually added to incubations in large excess. Many factors have been shown to influence UGT activity and kinetics in vitro, and these must be accounted for during experimental design and data interpretation. While the assessment of drug-drug interactions resulting from UGT inhibition has been challenging in the past, the increasing availability of UGT enzyme-selective substrate and inhibitor "probes" provides the prospect for more reliable reaction phenotyping and assessment of drug-drug interaction potential. Although extrapolation of the in vitro intrinsic clearance of a glucuronidated drug often underpredicts in vivo clearance, careful selection of in vitro experimental conditions and inclusion of extrahepatic glucuronidation may improve the predictivity of in vitro-in vivo extrapolation. Physiologically based pharmacokinetic (PBPK) modeling has also shown to be of value for predicting PK of drugs eliminated by glucuronidation.

Synthesis, Biological, and Computational Evaluation of Antagonistic, Chiral Hydrobenzoin Esters of Arecaidine Targeting mAChR M1

Muscarinic acetylcholine receptors (mAChRs) are a pivotal constituent of the central and peripheral nervous system. Yet, therapeutic and diagnostic applications thereof are hampered by the lack of subtype selective ligands. Within this work, we synthesized and chemically characterized three different stereoisomers of hydrobenzoin esters of arecaidine by NMR, HR-MS, chiral chromatography, and HPLC-logP. All compounds are structurally eligible for carbon-11 labeling and show appropriate stability in Dulbecco’s phosphate-buffered saline (DPBS) and F12 cell culture medium. A competitive radioligand binding assay on Chinese hamster ovary cell membranes comprising the human mAChR subtypes M1-M5 showed the highest orthosteric binding affinity for subtype M1 and a strong influence of stereochemistry on binding affinity, which corresponds to in silico molecular docking experiments. K_i values toward M1 were determined as 99 ± 19 nM, 800 ± 200 nM, and 380 ± 90 nM for the (R,R)-, (S,S)-, and racemic (R,S)-stereoisomer, respectively, highlighting the importance of stereochemical variations in mAChR ligand development. All three stereoisomers were shown to act as antagonists toward mAChR M1 using a Fluo-4 calcium efflux assay. With respect to future positron emission tomography (PET) tracer development, the (R,R)-isomer appears especially promising as a lead structure due to its highest subtype selectivity and lowest K_i value.

Evidence-based strategies for the characterisation of human drug and chemical glucuronidation in vitro and UDP-glucuronosyltransferase reaction phenotyping

Enzymes of the UDP-glucuronosyltransferase (UGT) superfamily contribute to the elimination of drugs from almost all therapeutic classes. Awareness of the importance of glucuronidation as a drug clearance mechanism along with increased knowledge of the enzymology of drug and chemical metabolism has stimulated interest in the development and application of approaches for the characterisation of human drug glucuronidation in vitro, in particular reaction phenotyping (the fractional contribution of the individual UGT enzymes responsible for the glucuronidation of a given drug), assessment of metabolic stability, and UGT enzyme inhibition by drugs and other xenobiotics. In turn, this has permitted the implementation of in vitro - in vivo extrapolation approaches for the prediction of drug metabolic clearance, intestinal availability, and drug-drug interaction liability, all of which are of considerable importance in pre-clinical drug development. Indeed, regulatory agencies (FDA and EMA) require UGT reaction phenotyping for new chemical entities if glucuronidation accounts for ≥25% of total metabolism. In vitro studies are most commonly performed with recombinant UGT enzymes and human liver microsomes (HLM) as the enzyme sources. Despite the widespread use of in vitro approaches for the characterisation of drug and chemical glucuronidation by HLM and recombinant enzymes, evidence-based guidelines relating to experimental approaches are lacking. Here we present evidence-based strategies for the characterisation of drug and chemical glucuronidation in vitro, and for UGT reaction phenotyping. We anticipate that the strategies will inform practice, encourage development of standardised experimental procedures where feasible, and guide ongoing research in the field.

Solving the crisis in psychopharmacological research: Cellular-membrane(s) pharmacology to the rescue?

There is an urgent need for the introduction of novel and better (i.e., improved risk-benefit profile) compounds for the treatment of major psychiatric disorders, in particular mood and psychotic disorders. However, despite increased societal awareness and a rising public and professional demand for such agents from patients and physicians, the pharmaceutical industry continues to close down its psychopharmacology research facilities in reaction to the lack of success with the search for new psychotropics. It is high time to stop this untoward trend and explore "new" lines of investigation to solve the current crisis in psychopharmacological research. In line with the prevailing molecular view in drug research in general, also in psychopharmacology mechanistic explanations for drug effects are "traditionally" looked for at the level of molecular targets, like receptors and transporters. Also, more recent approaches, although using so-called systems- and function-based approaches to model the multidimensional characteristics of psychiatric disorders and psychotropic drug action, still emphasize this search strategy for new therapeutic leads by identification of single molecules or molecular pathways. This "psychomolecular gaze" overlooks and disregards the fact that psychotropic agents usually are highly hydrophobic and amphipathic/amphiphilic agents that, in addition to their interaction with membrane-bound proteins in the form of e.g. receptors or transporters, also interact strongly with the lipid component of cellular membranes. Here we suggest to develop a program of systematic, whole-cell level based, investigation into the role of these physical-chemical cellular membrane interactions in the therapeutic action of known psychotherapeutics. This complementary yet conceptually different approach, in our opinion, will complement drug development in psychopharmacology and thereby assist in overcoming the current crisis. In this way the "old" physical theory of drug action, which antedates the current, primary molecular, paradigm may offer "new" options for lead discovery in psychopharmacological research.

Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions

Cytochrome P450 (CYP) enzyme kinetics often do not conform to Michaelis-Menten assumptions, and time-dependent inactivation (TDI) of CYPs displays complexities such as multiple substrate binding, partial inactivation, quasi-irreversible inactivation, and sequential metabolism. Additionally, in vitro experimental issues such as lipid partitioning, enzyme concentrations, and inactivator depletion can further complicate the parameterization of in vitro TDI. The traditional replot method used to analyze in vitro TDI datasets is unable to handle complexities in CYP kinetics, and numerical approaches using ordinary differential equations of the kinetic schemes offer several advantages. Improvement in the parameterization of CYP in vitro kinetics has the potential to improve prediction of clinical drug-drug interactions (DDIs). This manuscript discusses various complexities in TDI kinetics of CYPs, and numerical approaches to model these complexities. The extrapolation of CYP in vitro TDI parameters to predict in vivo DDIs with static and dynamic modeling is discussed, along with a discussion on current gaps in knowledge and future directions to improve the prediction of DDI with in vitro data for CYP catalyzed drug metabolism.

Simplified Method to Determine the Efflux Ratio on P-Glycoprotein Substrates Using Three-Compartment Model Analysis for Caco-2 Cell Assay Data

PURPOSE

Multiple time-point sampling is required in transcellular transport studies to accurately calculate the appropriate efflux ratio (ER). Our study sought to develop a simplified method to determine the ER in Caco-2 cells.

METHODS

The equation for the ER was derived from a three-compartment model of apical to basal and basal to apical transport. Transcellular transport studies were conducted with 10 non-P-glycoprotein (P-gp) and 6 P-gp substrates in Caco-2 cells, and the ER was calculated using this equation.

RESULTS

The equation for the ER used the concentration ratio in the receiver compartment at the same time-point; therefore, the ER can theoretically be calculated using only a single point. The ER of all non-P-gp substrates tested was close to 1 at all sampling times. The ERs of cyclosporine A calculated from the concentration ratio at 30, 60, 90, and 120 min incubation were 2.93, 6.43, 7.12, and 9.57, respectively, and the ER at 120 min was almost identical to the theoretical value (9.62) calculated using three-compartment model analysis. The other 5 P-gp substrates showed a similar tendency. Single-point sampling can be used to accurately calculate ER at 120 min.

CONCLUSIONS

Single-point sampling is a promising approach for calculating appropriate ERs in the drug discovery stage.

A Comparative Study of in vitro Assays for Predicting the Nonspecific Binding of PET Imaging Agents in vivo

Nonspecific binding (NSB) is a key parameter in optimizing PET imaging tracers. We compared the ability to predict NSB of three available methods: LIMBA, rat f_u,brain , and CHI(IAM). Even though NSB is often associated with lipophilicity, we observed that logD does not correlate with any of these assays, clearly indicating that lipophilicity, while influencing NSB, is insufficient to predict it. A cross-comparison of the methods showed that all three correlate and are useful predictors of NSB. The three assays, however, rank the molecules slightly differently, illustrating the challenge of comparing molecules within a narrow chemical space. We also noted that CHI(IAM) values more effectively predict V_NS , a measure of in vivo NSB in the human brain. CHI(IAM) measurements might be a closer model of the actual physicochemical interaction between PET tracer candidates and cell membranes, and seems to be the method of choice for the optimization of in vivo NSB.

Probing protein-protein and protein-substrate interactions in the dynamic membrane-associated ternary complex of cytochromes P450, b5, and reductase

Cytochrome P450 (cytP450) interacts with two redox partners, cytP450 reductase and cytochrome-b5, to metabolize substrates. Using NMR, we reveal changes in the dynamic interplay when all three proteins are incorporated into lipid nanodiscs in the absence and presence of substrates.

Prediction of Tissue-Plasma Partition Coefficients Using Microsomal Partitioning: Incorporation into Physiologically based Pharmacokinetic Models and Steady-State Volume of Distribution Predictions

Drug distribution is a necessary component of models to predict human pharmacokinetics. A new membrane-based tissue-plasma partition coefficient (K p) method (K p,mem) to predict unbound tissue to plasma partition coefficients (K pu) was developed using in vitro membrane partitioning [fraction unbound in microsomes (f um)], plasma protein binding, and log P The resulting K p values were used in a physiologically based pharmacokinetic (PBPK) model to predict the steady-state volume of distribution (V ss) and concentration-time (C-t) profiles for 19 drugs. These results were compared with K p predictions using a standard method [the differential phospholipid K p prediction method (K p,dPL)], which differentiates between acidic and neutral phospholipids. The K p,mem method was parameterized using published rat K pu data and tissue lipid composition. The K pu values were well predicted with R 2 = 0.8. When used in a PBPK model, the V ss predictions were within 2-fold error for 12 of 19 drugs for K p,mem versus 11 of 19 for Kp,dPL With one outlier removed for K p,mem and two for K p,dPL, the V ss predictions for R 2 were 0.80 and 0.79 for the K p,mem and K p,dPL methods, respectively. The C-t profiles were also predicted and compared. Overall, the K p,mem method predicted the V ss and C-t profiles equally or better than the K p,dPL method. An advantage of using f um to parameterize membrane partitioning is that f um data are used for clearance prediction and are, therefore, generated early in the discovery/development process. Also, the method provides a mechanistically sound basis for membrane partitioning and permeability for further improving PBPK models. SIGNIFICANCE STATEMENT: A new method to predict tissue-plasma partition coefficients was developed. The method provides a more mechanistic basis to model membrane partitioning.

Impact of Lipid Partitioning on the Design, Analysis, and Interpretation of Microsomal Time-Dependent Inactivation

Nonspecific drug partitioning into microsomal membranes must be considered for in vitro-in vivo correlations. This work evaluated the effect of including lipid partitioning in the analysis of complex TDI kinetics with numerical methods. The covariance between lipid partitioning and multiple inhibitor binding was evaluated. Simulations were performed to test the impact of lipid partitioning on the interpretation of TDI kinetics, and experimental TDI datasets for paroxetine (PAR) and itraconazole (ITZ) were modeled. For most kinetic schemes, modeling lipid partitioning results in statistically better fits. For MM-IL simulations (KI,u = 0.1 µM, kinact = 0.1 minute-1), concurrent modeling of lipid partitioning for an fumic range (0.01, 0.1, and 0.5) resulted in better fits compared with post hoc correction (AICc: -526 vs. -496, -579 vs. -499, and -636 vs. -579, respectively). Similar results were obtained with EII-IL. Lipid partitioning may be misinterpreted as double binding, leading to incorrect parameter estimates. For the MM-IL datasets, when fumic = 0.02, MM-IL, and EII model fits were indistinguishable (δAICc = 3). For less partitioned datasets (fumic = 0.1 or 0.5), the inclusion of partitioning resulted in better models. The inclusion of lipid partitioning can lead to markedly different estimates of KI,u and kinact A reasonable alternate experimental design is nondilution TDI assays, with post hoc fumic incorporation. The best fit models for PAR (MIC-M-IL) and ITZ (MIC-EII-M-IL and MIC-EII-M-Seq-IL) were consistent with their reported mechanism and kinetics. Overall, experimental fumic values should be concurrently incorporated into TDI models with complex kinetics, when dilution protocols are used.

Interaction of natural compounds with biomembrane models: A biophysical approach for the Alzheimer's disease therapy

Natural compounds such as caffeine (CA), gallic acid (GA) and tannic acid (TA) have been reported to be useful for Alzheimer's disease (AD) therapy. It was proved that some natural compounds inhibit the formation of senil plaques composed by beta-amyloid peptide (Aβ), a hallmark of AD. Evidences suggest that the therapeutic activity of compounds depends of their interaction with biological membranes. To understand why these compounds fail in vivo and in clinical trials, it is important to evaluate their pharmacokinetics properties. Thus, a biophysical approach to study drug-membrane interactions is essential to understand the mechanisms by which the drugs interact with the cellular membranes and affect the Aβ production, aggregation and clearance pathways. 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) and cholesterol (chol) were used to mimic the biophysical properties of cell membranes and study their interactions with these compounds. The partition coefficient, influence on membrane fluidity and location within the bilayer of the drugs were studied by derivative spectrophotometry, dynamic light scattering and fluorescence quenching, respectively. The results suggest that TA exhibited a significant higher partition than CA and GA and a preferential location near to the polar head of bilayer. The obtained results may explain the therapeutic mechanisms reported for these natural compounds.

Heterogeneous drug tissue binding in brain regions of rats, Alzheimer's patients and controls: impact on translational drug development

For preclinical and clinical assessment of therapeutically relevant unbound, free, brain concentrations, the pharmacokinetic parameter fraction of unbound drug in brain (f_u,brain) is commonly used to compensate total drug concentrations for nonspecific brain tissue binding (BTB). As, homogenous BTB is assumed between species and in health and disease, rat BTB is routinely used. The impact of Alzheimer’s disease (AD) on drug BTB in brain regions of interest (ROI), i.e., f_u,brain,ROI, is yet unclear. This study for the first time provides insight into regional drug BTB and the validity of employing rat f_u,brain,ROI as a surrogate of human BTB, by investigating five marketed drugs in post-mortem tissue from AD patients (n = 6) and age-matched controls (n = 6). Heterogeneous drug BTB was observed in all within group comparisons independent of disease and species. The findings oppose the assumption of uniform BTB, highlighting the need of case-by-case evaluation of f_u,brain,ROI in translational CNS research.

Computational modeling of drug transport across the in vitro cornea

A novel quasi-3D (Q3D) modeling approach was developed to model networks of one dimensional structures like tubes and vessels common in human anatomy such as vascular and lymphatic systems, neural networks, and respiratory airways. Instead of a branching network of the same tissue type, this approach was extended to model an interconnected stack of different corneal tissue layers with membrane junction conditions assigned between the tissues. The multi-laminate structure of the cornea presents a unique barrier design and opportunity for investigation using Q3D modeling. A Q3D model of an in vitro rabbit cornea was created to simulate the drug transport across the cornea, accounting for transcellular and paracellular pathways of passive and convective drug transport as well as physicochemistry of lipophilic partitioning and protein binding. Lipophilic Rhodamine B and hydrophilic fluorescein were used as drug analogs. The model predictions for both hydrophilic and lipophilic tracers were able to match the experimental measurements along with the sharp discontinuities at the epithelium-stroma and stroma-endothelium interfaces. This new modeling approach was successfully applied towards pharmacokinetic modeling for use in topical ophthalmic drug design.

Functionalized 2,1-benzothiazine 2,2-dioxides as new inhibitors of Dengue NS5 RNA-dependent RNA polymerase

Over recent years, many RNA viruses have been "re-discovered", including life-threatening flaviviruses, such as Dengue, Zika, and several encephalitis viruses. Since no specific inhibitors are currently available to treat these infections, there is a pressing need for new therapeutics. Among the flaviviral proteins, NS5 RNA-dependent RNA polymerase (RdRp) represents a validated target being essential for viral replication and it has no human analog. To date, few NS5 RdRp inhibitor chemotypes have been reported and no inhibitors are currently in clinical development. In this context, after an in vitro screening against Dengue 3 NS5 RdRp of our in-house HCV NS5B inhibitors focused library, we found that 2,1-benzothiazine 2,2-dioxides are promising non-nucleoside inhibitors of flaviviral RdRp with compounds 8 and 10 showing IC₅₀ of 0.6 and 0.9 μM, respectively. Preliminary structure-activity relationships indicated a key role for the C-4 benzoyl group and the importance of a properly functionalized C-6 phenoxy moiety to modulate potency. Compound 8 acts as non-competitive inhibitor and its proposed pose in the so-called N pocket of the RdRp thumb domain allowed to explain the key contribution of the benzoyl and the phenoxy moieties for the ligand binding.

Continuous Intestinal Absorption Model Based on the Convection-Diffusion Equation

Prediction of the rate and extent of drug absorption upon oral dosing needs models that capture the complexities of both the drug molecule and intestinal physiology. We report here the development of a continuous intestinal absorption model based on the convection-diffusion equation. The model includes explicit enterocyte apical membrane and intracellular lipid radial compartments along the length of the intestine. Physiologic functions along length x are built into the model and include velocity, diffusion, surface areas, and pH of the intestine. Also included are expression levels of the intestinal active uptake transporter OATP2B1 and efflux transporter P-gp. Oral dosing of solution as well as solid (with a dissolution function) was modeled for several drugs. The fraction absorbed (FA) and concentration-time (C-t) profiles were predicted and compared with clinical data. Overall, FA was well predicted upon oral (n = 21) or colonic dosing (n = 11), with four outliers. The overall accuracy (prediction of the correct bin) was 81% with outliers and 90% without outliers. Of the nine solution dosing data sets, six drugs were very well predicted with an exposure overlap coefficient (EOC) > 0.9 and predicted C_max and T_max values similar to those observed. Of the six solid dose formulations evaluated, the EOC values were > 0.9 for all drugs except budesonide. The observed precipitation of nifedipine at high doses was predicted by the model. Most of the poor predictions were for drugs that are known to be transporter substrates. As proof of concept, incorporating OATP2B1 and P-gp markedly improved the EOC and predicted C_max and T_max for fexofenadine. Finally, the continuous intestinal model accurately recapitulated the known relationships between drug absorption and permeability, solubility, and particle size. Together, these results indicate that this preliminary intestinal absorption model offers a simple and straightforward framework to build in complexities such as drug permeability, lipid partitioning, solubility, metabolism, and transport for improved prediction of the rate and extent of drug absorption.

The catalytic function of cytochrome P450 is entwined with its membrane-bound nature

Cytochrome P450, a family of monooxygenase enzymes, is organized as a catalytic metabolon, which requires enzymatic partners as well as environmental factors that tune its complex dynamic. P450 and its reducing counterparts—cytochrome P450-reductase and cytochrome b ₅—are membrane-bound proteins located in the cytosolic side of the endoplasmic reticulum. They are believed to dynamically associate to form functional complexes. Increasing experimental evidence signifies the role(s) played by both protein-protein and protein-lipid interactions in P450 catalytic function and efficiency. However, the biophysical challenges posed by their membrane-bound nature have severely limited high-resolution understanding of the molecular interfaces of these interactions. In this article, we provide an overview of the current knowledge on cytochrome P450, highlighting the environmental factors that are entwined with its metabolic function. Recent advances in structural biophysics are also discussed, setting up the bases for a new paradigm in the study of this important class of membrane-bound enzymes.

Intracellular drug bioavailability: a new predictor of system dependent drug disposition

Intracellular drug exposure is influenced by cell- and tissue-dependent expression of drug-transporting proteins and metabolizing enzymes. Here, we introduce the concept of intracellular bioavailability (F_ic) as the fraction of extracellular drug available to bind intracellular targets, and we assess how F_ic is affected by cellular drug disposition processes. We first investigated the impact of two essential drug transporters separately, one influx transporter (OATP1B1; SLCO1B1) and one efflux transporter (P-gp; ABCB1), in cells overexpressing these proteins. We showed that OATP1B1 increased F_ic of its substrates, while P-gp decreased F_ic. We then investigated the impact of the concerted action of multiple transporters and metabolizing enzymes in freshly-isolated human hepatocytes in culture configurations with different levels of expression and activity of these proteins. We observed that F_ic was up to 35-fold lower in the configuration with high expression of drug-eliminating transporters and enzymes. We conclude that F_ic provides a measurement of the net impact of all cellular drug disposition processes on intracellular bioavailable drug levels. Importantly, no prior knowledge of the involved drug distribution pathways is required, allowing for high-throughput determination of drug access to intracellular targets in highly defined cell systems (e.g., single-transporter transfectants) or in complex ones (including primary human cells).

Effect of benzocaine and propranolol on phospholipid-based bilayers

Drug/bilayer interactions are fundamental in determining the action mechanism of active ingredients. Neutron techniques represent unique tools for having a clear comprehension of such interactions.

Drug Distribution. Part 1. Models to Predict Membrane Partitioning

PURPOSE

Tissue partitioning is an important component of drug distribution and half-life. Protein binding and lipid partitioning together determine drug distribution.

METHODS

Two structure-based models to predict partitioning into microsomal membranes are presented. An orientation-based model was developed using a membrane template and atom-based relative free energy functions to select drug conformations and orientations for neutral and basic drugs.

RESULTS

The resulting model predicts the correct membrane positions for nine compounds tested, and predicts the membrane partitioning for n = 67 drugs with an average fold-error of 2.4. Next, a more facile descriptor-based model was developed for acids, neutrals and bases. This model considers the partitioning of neutral and ionized species at equilibrium, and can predict membrane partitioning with an average fold-error of 2.0 (n = 92 drugs).

CONCLUSIONS

Together these models suggest that drug orientation is important for membrane partitioning and that membrane partitioning can be well predicted from physicochemical properties.

A Fragment-Based Approach for the Computational Prediction of the Nonspecific Binding of Drugs to Hepatic Microsomes

Correction for the nonspecific binding (NSB) of drugs to liver microsomes is essential for the accurate measurement of the kinetic parameters K_m and K_i, and hence in vitro-in vivo extrapolation to predict hepatic clearance and drug-drug interaction potential. Although a number of computational approaches for the estimation of drug microsomal NSB have been published, they generally rely on compound lipophilicity and charge state at the expense of other physicochemical and chemical properties. In this work, we report the development of a fragment-based hologram quantitative structure activity relationship (HQSAR) approach for the prediction of NSB using a database of 132 compounds. The model has excellent predictivity, with a noncross-validated r² of 0.966 and cross-validated r² of 0.680, with a predictive r² of 0.748 for an external test set comprising 34 drugs. The HQSAR method reliably predicted the fraction unbound in incubations of 95% of the training and test set drugs, excluding compounds with a steroid or morphinan 4,5-epoxide nucleus. Using the same data set of compounds, performance of the HQSAR method was superior to a model based on logP/D as the sole descriptor (predictive r² for the test set compounds, 0.534). Thus, the HQSAR method provides an alternative approach to laboratory-based procedures for the prediction of the NSB of drugs to liver microsomes, irrespective of the drug charge state (acid, base, or neutral).

Intracellular Unbound Atorvastatin Concentrations in the Presence of Metabolism and Transport

Accurate prediction of drug target activity and rational dosing regimen design require knowledge of drug concentrations at the target. It is important to understand the impact of processes such as membrane permeability, partitioning, and active transport on intracellular drug concentrations. The present study aimed to predict intracellular unbound atorvastatin concentrations and characterize the effect of enzyme-transporter interplay on these concentrations. Single-pass liver perfusion studies were conducted in rats using atorvastatin (ATV, 1 µM) alone at 4°C and at 37°C in presence of rifampin (RIF, 20 µM) and 1-aminobenzotriazole (ABT, 1 mM), separately and in combination. The unbound intracellular ATV concentration was predicted with a five-compartment explicit membrane model using the parameterized diffusional influx clearance, active basolateral uptake clearance, and metabolic clearance. Chemical inhibition of uptake and metabolism at 37°C proved to be better controls relative to studies at 4°C. The predicted unbound intracellular concentration at the end of the 50-minute perfusion in the +ABT , +ABT+RIF, and the ATV-only groups was 6.5 µM, 0.58 µM, and 5.14 µM, respectively. The predicted total liver concentrations and amount recovered in bile were within 0.94-1.3 fold of the observed value in all groups. The fold difference in total liver concentration did not always extrapolate to the fold difference in predicted unbound concentration across groups. Together, these results support the use of compartmental modeling to predict intracellular concentrations in dynamic organ-based systems. These predictions can provide insight into the role of uptake transporters and metabolizing enzymes in determining drug tissue concentrations.

Assessment of Free Drug Concentration in Cyclodextrin Formulations Is Essential to Determine Drug Potency in Functional In Vitro Assays

Cyclodextrins (CD) have the ability to form inclusion complexes with drugs and can be used as excipients to enhance solubility of poorly soluble drugs. To make accurate estimations of the potency of the drug, knowledge of the free drug concentration is important. The aim of this study was to evaluate the applicability of calculated free drug concentrations toward response measurements in a transient receptor potential vanilloid receptor-1 cell-based in vitro assay. This included accounting for potential competitive CD binding of 2 transient receptor potential vanilloid receptor-1 active entities: 1 antagonist, and 1 agonist (capsaicin). Solubility of the CD-drug complexes was measured, and the ligand to substrate affinity in CD formulations was determined according to the phase-solubility technique. The total concentration of antagonist, agonist, CD, and the binding constants between ligands and CD were used to calculate the free concentration of CD ligands. For capsaicin and 2 of the 3 investigated model drugs, the calculated free drug concentration was consistent with the experimental in vitro data while it was overestimated for one of the compounds. In conclusion, the suggested approach can be used to calculate free drug concentration and competitive binding in CD formulations for the application of cell-based drug functionality assays.

In silico pharmacology: Drug membrane partitioning and crossing

Over the past decade, molecular dynamics (MD) simulations have become particularly powerful to rationalize drug insertion and partitioning in lipid bilayers. MD simulations efficiently support experimental evidences, with a comprehensive understanding of molecular interactions driving insertion and crossing. Prediction of drug partitioning is discussed with respect to drug families (anesthetics; β-blockers; non-steroidal anti-inflammatory drugs; antioxidants; antiviral drugs; antimicrobial peptides). To accurately evaluate passive permeation coefficients turned out to be a complex theoretical challenge; however the recent methodological developments based on biased MD simulations are particularly promising. Particular attention is paid to membrane composition (e.g., presence of cholesterol), which influences drug partitioning and permeation. Recent studies concerning in silico models of membrane proteins involved in drug transport (influx and efflux) are also reported here. These studies have allowed gaining insight in drug efflux by, e.g., ABC transporters at an atomic resolution, explicitly accounting for the mandatory forces induced by the surrounded lipid bilayer. Large-scale conformational changes were thoroughly analyzed.

The Role of Protein-Protein and Protein-Membrane Interactions on P450 Function

This symposium summary, sponsored by the ASPET, was held at Experimental Biology 2015 on March 29, 2015, in Boston, Massachusetts. The symposium focused on: 1) the interactions of cytochrome P450s (P450s) with their redox partners; and 2) the role of the lipid membrane in their orientation and stabilization. Two presentations discussed the interactions of P450s with NADPH-P450 reductase (CPR) and cytochrome b5. First, solution nuclear magnetic resonance was used to compare the protein interactions that facilitated either the hydroxylase or lyase activities of CYP17A1. The lyase interaction was stimulated by the presence of b5 and 17α-hydroxypregnenolone, whereas the hydroxylase reaction was predominant in the absence of b5. The role of b5 was also shown in vivo by selective hepatic knockout of b5 from mice expressing CYP3A4 and CYP2D6; the lack of b5 caused a decrease in the clearance of several substrates. The role of the membrane on P450 orientation was examined using computational methods, showing that the proximal region of the P450 molecule faced the aqueous phase. The distal region, containing the substrate-access channel, was associated with the membrane. The interaction of NADPH-P450 reductase (CPR) with the membrane was also described, showing the ability of CPR to "helicopter" above the membrane. Finally, the endoplasmic reticulum (ER) was shown to be heterogeneous, having ordered membrane regions containing cholesterol and more disordered regions. Interestingly, two closely related P450s, CYP1A1 and CYP1A2, resided in different regions of the ER. The structural characteristics of their localization were examined. These studies emphasize the importance of P450 protein organization to their function.

Membrane-drug interactions studied using model membrane systems

The direct interaction of drugs with the cell membrane is often neglected when drug effects are studied. Systematic investigations are hindered by the complexity of the natural membrane and model membrane systems can offer a useful alternative. Here some examples are reviewed of how model membrane architectures including vesicles, Langmuir monolayers and solid supported membranes can be used to investigate the effects of drug molecules on the membrane structure, and how these interactions can translate into effects on embedded membrane proteins.

The Reliability of Estimating Ki Values for Direct, Reversible Inhibition of Cytochrome P450 Enzymes from Corresponding IC50 Values: A Retrospective Analysis of 343 Experiments

In the present study, we conducted a retrospective analysis of 343 in vitro experiments to ascertain whether observed (experimentally determined) values of Ki for reversible cytochrome P450 (P450) inhibition could be reliably predicted by dividing the corresponding IC₅₀ values by two, based on the relationship (for competitive inhibition) in which Ki = IC₅₀/2 when [S] (substrate concentration) = Km (Michaelis-Menten constant). Values of Ki and IC₅₀ were determined under the following conditions: 1) the concentration of P450 marker substrate, [S], was equal to Km (for IC₅₀ determinations) and spanned Km (for Ki determinations); 2) the substrate incubation time was short (5 minutes) to minimize metabolism-dependent inhibition and inhibitor depletion; and 3) the concentration of human liver microsomes was low (0.1 mg/ml or less) to maximize the unbound fraction of inhibitor. Under these conditions, predicted Ki values, based on IC₅₀/2, correlated strongly with experimentally observed Ki determinations [r = 0.940; average fold error (AFE) = 1.10]. Of the 343 predicted Ki values, 316 (92%) were within a factor of 2 of the experimentally determined Ki values, and only one value fell outside a 3-fold range. In the case of noncompetitive inhibitors, Ki values predicted from IC₅₀/2 values were overestimated by a factor of nearly 2 (AFE = 1.85; n = 13), which is to be expected because, for noncompetitive inhibition, Ki = IC₅₀ (not IC₅₀/2). The results suggest that, under appropriate experimental conditions with the substrate concentration equal to Km, values of Ki for direct, reversible inhibition can be reliably estimated from values of IC₅₀/2.

Toward predicting drug-induced liver injury: parallel computational approaches to identify multidrug resistance protein 4 and bile salt export pump inhibitors

Drug-induced liver injury (DILI) is an important cause of drug toxicity. Inhibition of multidrug resistance protein 4 (MRP4), in addition to bile salt export pump (BSEP), might be a risk factor for the development of cholestatic DILI. Recently, we demonstrated that inhibition of MRP4, in addition to BSEP, may be a risk factor for the development of cholestatic DILI. Here, we aimed to develop computational models to delineate molecular features underlying MRP4 and BSEP inhibition. Models were developed using 257 BSEP and 86 MRP4 inhibitors and noninhibitors in the training set. Models were externally validated and used to predict the affinity of compounds toward BSEP and MRP4 in the DrugBank database. Compounds with a score above the median fingerprint threshold were considered to have significant inhibitory effects on MRP4 and BSEP. Common feature pharmacophore models were developed for MRP4 and BSEP with LigandScout software using a training set of nine well characterized MRP4 inhibitors and nine potent BSEP inhibitors. Bayesian models for BSEP and MRP4 inhibition/noninhibition were developed with cross-validated receiver operator curve values greater than 0.8 for the test sets, indicating robust models with acceptable false positive and false negative prediction rates. Both MRP4 and BSEP inhibitor pharmacophore models were characterized by hydrophobic and hydrogen-bond acceptor features, albeit in distinct spatial arrangements. Similar molecular features between MRP4 and BSEP inhibitors may partially explain why various drugs have affinity for both transporters. The Bayesian (BSEP, MRP4) and pharmacophore (MRP4, BSEP) models demonstrated significant classification accuracy and predictability.

Compartmental models for apical efflux by P-glycoprotein: part 2--a theoretical study on transporter kinetic parameters

PURPOSE

The impact of efflux transporters in intracellular concentrations of a drug can be predicted with modeling techniques. In Part 1, several compartmental models were developed and evaluated. The goal of Part 2 was to apply these models to the characterization and interpretation of saturation kinetic data.

METHODS

The compartmental models from Part 1 were used to evaluate a previously published dataset from cell lines expressing varying levels of P-glycoprotein. Kinetic parameters for the transporter were estimated and compared across models.

RESULTS

Fits and errors for all compartmental models were identical. All compartmental models predicted more consistent parameters than the Michaelis-Menten model. The 5-compartment model with efflux out of the membrane predicted differential impact of P-gp upon apical versus basolateral drug exposure. Finally, the saturable kinetics of active efflux along with a permeability barrier was modeled to delineate a relationship between intracellular concentration with or without active efflux versus donor concentration. This relationship was not a rectangular hyperbola, but instead was shown to be a quadratic function.

CONCLUSIONS

One approach to estimate an in vivo transporter effect is to first model an intracellular Km value from in vitro data, and use this value along with the appropriate tissue transporter expression levels and relative surface area to calculate the relevant apparent Km (or Ki) values. Together with the results from Part 1, these studies suggest that compartmental models can provide a path forward to better utilize in vitro transporter data for in vivo predictions such as physiologically based pharmacokinetic modeling.

Compartmental models for apical efflux by P-glycoprotein--part 1: evaluation of model complexity

PURPOSE

With the goal of quantifying P-gp transport kinetics, Part 1 of these manuscripts evaluates different compartmental models and Part 2 applies these models to kinetic data.

METHODS

Models were developed to simulate the effect of apical efflux transporters on intracellular concentrations of six drugs. The effect of experimental variability on model predictions was evaluated. Several models were evaluated, and characteristics including membrane configuration, lipid content, and apical surface area (asa) were varied.

RESULTS

Passive permeabilities from MDCK-MDR1 cells in the presence of cyclosporine gave lower model errors than from MDCK control cells. Consistent with the results in Part 2, model configuration had little impact on calculated model errors. The 5-compartment model was the simplest model that reproduced experimental lag times. Lipid content and asa had minimal effect on model errors, predicted lag times, and intracellular concentrations. Including endogenous basolateral uptake activity can decrease model errors. Models with and without explicit membrane barriers differed markedly in their predicted intracellular concentrations for basolateral drug exposure. Single point data resulted in clearances similar to time course data.

CONCLUSIONS

Compartmental models are useful to evaluate the impact of efflux transporters on intracellular concentrations. Whereas a 3-compartment model may be sufficient to predict the impact of transporters that efflux drugs from the cell, a 5-compartment model with explicit membranes may be required to predict intracellular concentrations when efflux occurs from the membrane. More complex models including additional compartments may be unnecessary.

Consideration of the unbound drug concentration in enzyme kinetics

The study of enzyme kinetics in drug metabolism involves assessment of rates of metabolism and inhibitory potencies over a suitable concentration range. In all but the very simplest in vitro system, these drug concentrations can be influenced by a variety of nonspecific binding reservoirs that can reduce the available concentration to the enzyme system under investigation. As a consequence, the apparent kinetic parameters that are derived, such as K m or K i, can deviate from the true values. There are a number of sources of these nonspecific binding depots or barriers, including membrane permeation and partitioning, plasma or serum protein binding, and incubational binding. In the latter case, this includes binding to the assay apparatus, as well as biological depots, depending on the characteristics of the in vitro matrix being used. Given the wide array of subcellular, cellular, and recombinant enzyme systems utilized in drug metabolism, each of these has different components that can influence the free drug concentration. The physicochemical properties of the test compound are also paramount in determining the influential factors in any deviation between true and apparent kinetic behavior. This chapter describes the underlying mechanisms determining the free drug concentration in vitro and how these factors can be accounted for in drug metabolism studies, illustrated with case studies from the literature.