Lack of appreciable species differences in nonspecific microsomal binding

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.

HLM-beads: Rapid Assessment of Nonspecific Binding to Human Liver Microsomes Using Magnetizable Beads

In early drug development, drug-drug interaction risk is routinely assessed using human liver microsomes (HLMs). Nonspecific binding of drugs to HLMs can affect the determination of accurate enzyme parameters (K_m, K_i, K_I). Previously, we described a novel in vitro model consisting of HLMs bound to magnetizable beads [HLM-magnetizable-beads system (HLM-beads)]. The HLM-beads enable rapid separation of HLMs from incubation media by applying a magnetic field. Here, HLM-beads were further characterized and evaluated as a tool to assess HLM nonspecific binding of small molecules. The free fractions (f_u,mic) of 13 compounds (chosen based on their pKa values) were determined using HLM-beads under three HLM concentrations (0.025, 0.50, and 1.0 mg/ml) and compared with those determined by equilibrium dialysis. Most f_u,mic values obtained using HLM-beads were within 0.5- to 2-fold of the values determined using equilibrium dialysis. The highest fold difference were observed for high binders itraconazole and BIRT2584 (1.9- to 2.9-fold), as the pronounced adsorption of these compounds to the equilibrium dialysis apparatus interfered with their f_u,mic determination. Correlation and linear regression analysis of the f_u,mic values generated using HLM-beads and equilibrium dialysis was conducted. Overall, a good correlation of f_u,mic values obtained by the two methods were observed, as the r and R² values from correlational analysis and linear regression analysis were >0.9 and >0.89, respectively. These studies demonstrate that HLM-beads can produce comparable f_u,mic values as determined by equilibrium dialysis while reducing the time required for this type of study from hours to only 10 minutes and compound apparatus adsorption. SIGNIFICANCE STATEMENT: This work introduces a new method of rapidly assessing nonspecific microsomal binding using human liver microsomes bound to magnetizable beads.

Investigation into MAO B-Mediated Formation of CC112273, a Major Circulating Metabolite of Ozanimod, in Humans and Preclinical Species: Stereospecific Oxidative Deamination of (S)-Enantiomer of Indaneamine (RP101075) by MAO B

Ozanimod, recently approved for treating relapsing multiple sclerosis, produced a disproportionate, active, MAO B-catalyzed metabolite (CC112273) that showed remarkable interspecies differences and led to challenges in safety testing. This study explored the kinetics of CC112273 formation from its precursor RP101075. Incubations with human liver mitochondrial fractions revealed K _Mapp, V _max, and intrinsic clearance (Cl_int) for CC112273 formation to be 4.8 μM, 50.3 pmol/min/mg protein, and 12 μl/min/mg, respectively, whereas Michaelis-Menten constant (K _M) with human recombinant MAO B was 1.1 μM. Studies with liver mitochondrial fractions from preclinical species led to K _Mapp, V _max, and Cl_int estimates of 3.0, 35, and 33 μM, 80.6, 114, 37.3 pmol/min/mg, and 27.2, 3.25, and 1.14 μl/min/mg in monkey, rat, and mouse, respectively, and revealed marked differences between rodents and primates, primarily attributable to differences in the K _M Comparison of Cl_int estimates revealed monkey to be ∼2-fold more efficient and the mouse and rat to be 11- and 4-fold less efficient than humans in CC112273 formation. The influence of stereochemistry on MAO B-mediated oxidation was also investigated using the R-isomer of RP101075 (RP101074). This showed marked selectivity toward catalysis of the S-isomer (RP101075) only. Docking into MAO B crystal structure suggested that although both the isomers occupied its active site, only the orientation of RP101075 presented the C-H on the α-carbon that was ideal for the C-H bond cleavage, which is a requisite for oxidative deamination. These studies explain the basis for the observed interspecies differences in the metabolism of ozanimod as well as the substrate stereospecificity for formation of CC112273. SIGNIFICANCE STATEMENT: This study evaluates the enzymology and the species differences of the major circulating metabolite of ozanimod, CC112273. Additionally, the study also explores the influence of stereochemistry on MAO B-catalyzed reactions. The study is of significance to the DMD readers given that this oxidation is catalyzed by a non-cytochrome P450 enzyme, and that marked species difference and notable stereospecificity was observed in MAO B-catalyzed biotransformation when the indaneamine enantiomers were used as substrates.

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.

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.

Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay

The in vitro-in vivo extrapolation (IVIVE) approach for predicting total plasma clearance (CL_tot) has been widely used to rank order compounds early in discovery. More recently, a computational machine learning approach utilizing physicochemical descriptors and fingerprints calculated from chemical structure information has emerged, enabling virtual predictions even earlier in discovery. Previously, this approach focused more on in vitro intrinsic clearance (CL_int) prediction. Herein, we directly compare these two approaches for predicting CL_tot in rats. A structurally diverse set of 1114 compounds with known in vivo CL_tot, in vitro CL_int, and plasma protein binding was used as the basis for this evaluation. The machine learning models were assessed by validation approaches using the time- and cluster-split training and test sets, and five-fold cross validation. Assessed by five-fold validation, the random forest regression (RF) and radial basis function (RBF) models demonstrated better prediction performance in eight attempted machine learning models. The CL_tot values predicted by the RF and RBF models were within two-fold of the observed values for 67.7 and 71.9% of cluster-split test set compounds, respectively, while the predictivity was worse in the time-split dataset. The predictivity of both models tended to be improved by incorporating in vitro parameters, unbound fraction in plasma (f_u,p), and CL_int. CL_tot prediction utilizing in vitro CL_int and the well-stirred model, correcting for the fraction unbound in blood, was substantially worse compared to machine learning approaches for the same cluster-split test set. The reason that CL_tot is underestimated by IVIVE is not fully explained by considering the calculated microsomal unbound fraction (cf_u,mic), extended clearance classification system (ECCS), and omitting high clearance compounds in excess of hepatic blood flow. The analysis suggests that in silico machine learning models may have the power to reduce reliance on or replace in vitro and in vivo studies for chemical structure optimization in early drug discovery.

Prediction of Fraction Unbound in Microsomal and Hepatocyte Incubations: A Comparison of Methods across Industry Datasets

The fraction unbound in the incubation, f_u,inc, is an important parameter to consider in the evaluation of intrinsic clearance measurements performed in vitro in hepatocytes or microsomes. Reliable estimates of f_u,inc based on a compound's structure have the potential to positively impact the screening timelines in drug discovery. Previous works suggested that f_u,inc is primarily driven by passive processes and can be described using physicochemical properties such as lipophilicity and the protonation state of the molecule. While models based on these principles proved predictive in relatively small datasets that included marketed drugs, their applicability domain has not been extensively explored. The work presented here from the in silico ADME discussion group (part of the International Consortium for Innovation through Quality in Pharmaceutical Development, the IQ consortium) describes the accuracy of these models in large proprietary datasets that include several thousand of compounds across chemical space. Overall, the models do well for compounds with low lipophilicity. In other words, the equations correctly predict that f_u,inc is, in general, above 0.5 for compounds with a calculated logP of less than 3. When applied to lipophilic compounds, the models failed to produce quantitatively accurate predictions of f_u,inc, with a high risk of underestimating binding properties. These models can, therefore, be used quantitatively for less lipophilic compounds. On the other hand, internal machine-learning models using a company's own proprietary dataset also predict compounds with higher lipophilicity reasonably well. Additionally, the data shown indicate that microsomal binding is, in general, a good proxy for hepatocyte binding.

Quantitative Prediction of Human Hepatic Clearance for P450 and Non-P450 Substrates from In Vivo Monkey Pharmacokinetics Study and In Vitro Metabolic Stability Tests Using Hepatocytes

Accurate prediction of human pharmacokinetics for drugs remains challenging, especially for non-cytochrome P450 (P450) substrates. Hepatocytes might be suitable for predicting hepatic intrinsic clearance (CL_int) of new chemical entities, because they can be applied to various compounds regardless of the metabolic enzymes. However, it was reported that hepatic CL_int is underestimated in hepatocytes. The purpose of the present study was to confirm the predictability of human hepatic clearance for P450 and non-P450 substrates in hepatocytes and the utility of animal scaling factors for the prediction using hepatocytes. CL_int values for 30 substrates of P450, UDP-glucuronosyltransferase, flavin-containing monooxygenase, esterases, reductases, and aldehyde oxidase in human microsomes, human S9 and human, rat, and monkey hepatocytes were estimated. Hepatocytes were incubated in serum of each species. Furthermore, CL_int values in human hepatocytes were corrected with empirical, monkey, and rat scaling factors. CL_int values in hepatocytes for most compounds were underestimated compared to observed values regardless of the metabolic enzyme, and the predictability was improved by using the scaling factors. The prediction using human hepatocytes corrected with monkey scaling factor showed the highest predictability for both P450 and non-P450 substrates among the predictions using liver microsomes, liver S9, and hepatocytes with or without scaling factors. CL_int values by this method for 80% and 90% of all compounds were within 2- and 3-fold of observed values, respectively. This method is accurate and useful for estimating new chemical entities, with no need to care about cofactors, localization of metabolic enzymes, or protein binding in plasma and incubation mixture.

Preclinical assessment of the ADME, efficacy and drug-drug interaction potential of a novel NAMPT inhibitor

GNE-617 (N-(4-((3,5-difluorophenyl)sulfonyl)benzyl)imidazo[1,2-a]pyridine-6-carboxamide) is a potent, selective nicotinamide phosphoribosyltransferase (NAMPT) inhibitor being explored as a potential treatment for human cancers. Plasma clearance was low in monkeys and dogs (9.14 mL min^-1kg^-1 and 4.62 mL min^-1kg^-1, respectively) and moderate in mice and rats (36.4 mL min^-1kg^-1 and 19.3 mL min^-1kg^-1, respectively). Oral bioavailability in mice, rats, monkeys and dogs was 29.7, 33.9, 29.4 and 65.2%, respectively. Allometric scaling predicted a low clearance of 3.3 mL min^-1kg^-1 and a volume of distribution of 1.3 L kg^-1 in human. Efficacy (57% tumor growth inhibition) in Colo-205 CRC tumor xenograft mice was observed at an oral dose of 15 mg/kg BID (AUC = 10.4 µM h). Plasma protein binding was moderately high. GNE-617 was stable to moderately stable in vitro. Main human metabolites identified in human hepatocytes were formed primarily by CYP3A4/5. Transporter studies suggested that GNE-617 is likely a substrate for MDR1 but not for BCRP. Simcyp^® simulations suggested a low (CYP2C9 and CYP2C8) or moderate (CYP3A4/5) potential for drug-drug interactions. The potential for autoinhibition was low. Overall, GNE-617 exhibited acceptable preclinical properties and projected human PK and dose estimates.

An examination of protein binding and protein-facilitated uptake relating to in vitro-in vivo extrapolation

As explained by the free drug theory, the unbound fraction of drug has long been thought to drive the efficacy of a molecule. Thus, the fraction unbound term, or f_u, appears in equations for fundamental pharmacokinetic parameters such as clearance, and is used when attempting in vitro to in vivo extrapolation (IVIVE). In recent years though, it has been noted that IVIVE does not always yield accurate predictions, and that some highly protein bound ligands have more efficient uptake than can be explained by their unbound fractions. This review explores the evolution of f_u terms included when implementing IVIVE, the concept of protein-facilitated uptake, and the mechanisms that have been proposed to account for facilitated uptake.

Discovery of Spiro Oxazolidinediones as Selective, Orally Bioavailable Inhibitors of p300/CBP Histone Acetyltransferases

p300 and its paralog CBP can acetylate histones and other proteins and have been implicated in a number of diseases characterized by aberrant gene activation, such as cancer. A novel, highly selective, orally bioavailable histone acetyltransferase (HAT) domain inhibitor has been identified through virtual ligand screening and subsequent optimization of a unique hydantoin screening hit. Conformational restraint in the form of a spirocyclization followed by substitution with a urea led to a significant improvement in potency. Replacement of the hydantoin moiety with an oxazolidinedione followed by fluoro substitution led to A-485, which exhibits potent cell activity, low clearance, and high oral bioavailability.

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).

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.