Optimization of a Higher Throughput Microsomal Stability Screening Assay for Profiling Drug Discovery Candidates

Design, physico-chemical properties and biological evaluation of some new N-[(phenoxy)alkyl]- and N-{2-[2-(phenoxy)ethoxy]ethyl}aminoalkanols as anticonvulsant agents

A series of thirty N-(phenoxy)alkyl or N-{2-[2-(phenoxy)ethoxy]ethyl}aminoalkanols has been designed, synthesized and evaluated for anticonvulsant activity in MES, 6Hz test, and pilocarpine-induced status epilepticus. Among the title compounds, the most promising seems R-(-)-2N-{2-[2-(2,6-dimethylphenoxy)ethoxy]ethyl}aminopropan-1-ol hydrochloride (22a) with proved absolute configuration with X-ray analysis and enantiomeric purity. The compound is effective in MES test with ED50=12.92 mg/kg b.w. and its rotarod TD50=33.26 mg/kg b.w. The activity dose is also effective in a neurogenic pain model-the formalin test. Within high throughput profile assay, among eighty one targets, the strongest affinity of the compound is observed towards σ receptors and 5-HT transporter and the compound does not bind to hERG. It also does not exhibit mutagenic properties in the Vibrio harveyi test. Moreover, murine liver microsomal assay and pharmacokinetics profile (mice, iv, p.o., ip) indicate that the liver is the primary site of biotransformation of the compound, suggesting that both 22a and its metabolite(s) are active, compensating probably low bioavailability of the parent molecule.

Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data

PURPOSE

Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability.

METHODS

Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism).

RESULTS

"Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h.

CONCLUSIONS

Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.

2-Acetyl-7-hydroxy-6-methoxy-1-methyl-1,2,3,4,-tetrahydroisoquinoline exhibits anti-inflammatory properties and protects the nigral dopaminergic neurons

Parkinson's disease (PD) is a neurodegenerative disorder characterized by degeneration of dopamine(DA)ergic neurons. Neuroinflammation caused by microglial activation is believed to be involved in the pathogenesis of neurodegenerative diseases including PD. In the present study, we tested the effects of a novel compound 2-acetyl-7-hydroxy-6-methoxy-1-methyl-1,2,3,4,-tetarhydroisoquinoline (AMTIQ) on neuroinflammatory response and DAergic neurodegeneration. In lipopolysaccharide-activated BV-2 microglial cells, AMTIQ lowered nitric oxide and tetrahydrobiopterin levels and downregulated gene expression of inducible nitric oxide synthase and GTP cyclohydrolase I. AMTIQ also repressed gene expression of the proinflammatory cytokines IL-1β and TNF-α, and attenuated nuclear translocation of NF-κB. AMTIQ was stable against liver microsomal enzymes from human and mouse and did not interfere with activities of the cytochrome p450 enzymes 1A2, 2D6, 2C9, 2C19 and 3A4. Pharmacokinetic studies revealed the brain to plasma ratio of AMTIQ to be 45%, suggesting it can penetrate the blood brain barrier. In 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-treated mouse PD model, AMTIQ led to decreased microglial activation, increased survival of DAergic neurons and their fibers, and improved behavioral scores on rotarod and vertical grid tests. Taken together, these results suggest that AMTIQ might serve as a candidate preventive-therapeutic agent for neurodegenerative diseases such as PD.

N-Alkylated arylsulfonamides of (aryloxy)ethyl piperidines: 5-HT(7) receptor selectivity versus multireceptor profile

The N-alkylation of the sulfonamide moiety, in a group of arylsulfonamide derivatives of (aryloxy)ethyl piperidines, may be considered as a strategy for the design of selective 5-HT7 receptor ligands or multifunctional agents to extend a polypharmacological approach to the treatment of complex diseases. The study allowed for the identification of 31 (1-methyl-N-{1-[2-(2-(t-butyl)phenoxy)ethyl]piperidin-4-yl}-N-cyclopropylmethyl-1H-pyrazole-4-sulfonamide), a potent and selective 5-HT7 receptor antagonist and 33 (1-methyl-N-{1-[2-(biphenyl-2-yloxy)ethyl]piperidin-4-yl}-N-cyclopropylmethyl-1H-pyrazole-4-sulfonamide), as multimodal 5-HT/dopamine receptor ligand, as 5-HT2A/5-HT7/D2 receptor antagonists. Both selected compounds were evaluated in vivo in a forced swim test (FST) in mice and in a novel object recognition (NOR) task in rats, demonstrating distinct antidepressant-like and pro-cognitive properties (MED=1.25 mg/kg and 1 mg/kg, ip, respectively). These findings warrant further studies to explore the therapeutic potential of N-alkylated arylsulfonamides for the treatment of CNS disorders.

Monoamine Oxidase Inhibitory Activity of Ferulic Acid Amides: Curcumin-Based Design and Synthesis

Ferulic acid has structural similarity with curcumin which is being reported for its monoamine oxidase (MAO) inhibitory activity. Based on this similarity, we designed a series of ferulic acid amides 6a-m and tested for their inhibitory activity on human MAO (hMAO) isoforms. All the compounds were found to inhibit the hMAO isoforms either selectively or non-selectively. Nine compounds (6a, 6b, 6g-m) were found to inhibit hMAO-B selectively, whereas the other four (6c-f) were found to be non-selective. There is a gradual shift from hMAO-B selectivity (6a,b) to non-selectivity (6c-f) as there is an increase in chain length at the amino terminus. In case of compounds having an aromatic nucleus at the amino terminus, increasing the carbon number between N and the aromatic ring increases the potency as well as selectivity toward hMAO-B. Compounds 6f, 6j, and 6k were subjected to membrane permeability and metabolic stability studies by in vitro assay methods. They were found to have a better pharmacokinetic profile than curcumin, ferulic acid, and selegiline. In order to understand the structural features responsible for the potency and selectivity of 6k, we carried out a molecular docking simulation study.

17β-Hydroxysteroid Dehydrogenase Type 2 Inhibition: Discovery of Selective and Metabolically Stable Compounds Inhibiting Both the Human Enzyme and Its Murine Ortholog

Design and synthesis of a new class of inhibitors for the treatment of osteoporosis and its comparative h17β-HSD2 and m17β-HSD2 SAR study are described. 17a is the first compound to show strong inhibition of both h17β-HSD2 and m17β-HSD2, intracellular activity, metabolic stability, selectivity toward h17β-HSD1, m17β-HSD1 and estrogen receptors α and β as well as appropriate physicochemical properties for oral bioavailability. These properties make it eligible for pre-clinical animal studies, prior to human studies.

Critically Assessing the Predictive Power of QSAR Models for Human Liver Microsomal Stability

To lower the possibility of late-stage failures in the drug development process, an up-front assessment of absorption, distribution, metabolism, elimination, and toxicity is commonly implemented through a battery of in silico and in vitro assays. As in vitro data is accumulated, in silico quantitative structure-activity relationship (QSAR) models can be trained and used to assess compounds even before they are synthesized. Even though it is generally recognized that QSAR model performance deteriorates over time, rigorous independent studies of model performance deterioration is typically hindered by the lack of publicly available large data sets of structurally diverse compounds. Here, we investigated predictive properties of QSAR models derived from an assembly of publicly available human liver microsomal (HLM) stability data using variable nearest neighbor (v-NN) and random forest (RF) methods. In particular, we evaluated the degree of time-dependent model performance deterioration. Our results show that when evaluated by 10-fold cross-validation with all available HLM data randomly distributed among 10 equal-sized validation groups, we achieved high-quality model performance from both machine-learning methods. However, when we developed HLM models based on when the data appeared and tried to predict data published later, we found that neither method produced predictive models and that their applicability was dramatically reduced. On the other hand, when a small percentage of randomly selected compounds from data published later were included in the training set, performance of both machine-learning methods improved significantly. The implication is that 1) QSAR model quality should be analyzed in a time-dependent manner to assess their true predictive power and 2) it is imperative to retrain models with any up-to-date experimental data to ensure maximum applicability.

Synthesis and biological evaluation of the 1-arylpyrazole class of σ(1) receptor antagonists: identification of 4-{2-[5-methyl-1-(naphthalen-2-yl)-1H-pyrazol-3-yloxy]ethyl}morpholine (S1RA, E-52862)

The synthesis and pharmacological activity of a new series of 1-arylpyrazoles as potent σ(1) receptor (σ(1)R) antagonists are reported. The new compounds were evaluated in vitro in human σ(1)R and guinea pig σ(2) receptor (σ(2)R) binding assays. The nature of the pyrazole substituents was crucial for activity, and a basic amine was shown to be necessary, in accordance with known receptor pharmacophores. A wide variety of amines and spacer lengths between the amino and pyrazole groups were tolerated, but only the ethylenoxy spacer and small cyclic amines provided compounds with sufficient selectivity for σ(1)R vs σ(2)R. The most selective compounds were further profiled, and compound 28, 4-{2-[5-methyl-1-(naphthalen-2-yl)-1H-pyrazol-3-yloxy]ethyl}morpholine (S1RA, E-52862), which showed high activity in the mouse capsaicin model of neurogenic pain, emerged as the most interesting candidate. In addition, compound 28 exerted dose-dependent antinociceptive effects in several neuropathic pain models. This, together with its good physicochemical, safety, and ADME properties, led compound 28 to be selected as clinical candidate.

In vitro metabolic stability and metabolite profiling of TJ0711 hydrochloride, a newly developed vasodilatory β-blocker, using a liquid chromatography-tandem mass spectrometry method

In this paper, a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed and validated for the simultaneous analysis of metabolic stability and metabolite profiling of 1-[4-(2-methoxyethyl) phenoxy]-3-[[2-(2-methoxyphenoxy) ethyl]amino]-2-propanol hydrochloride (TJ0711 HCl), a new vasodilatory β-blocker. Multiple reaction monitoring (MRM) was used as a survey scan to quantify the parent compound and to trigger the acquisition of enhanced product ions (EPI) for the identification of formed metabolites. In addition, comparison between MRM-only and MRM-information dependent acquisition-EPI (MRM-IDA-EPI) methods was conducted to determine analytical variables, including linearity, limit of detection (LOD), lower limit of quantification (LLOQ), as well as intra-day and inter-day accuracy and precision. Results demonstrated that MRM-IDA-EPI quantitative analysis was not affected by the addition of EPI scans to obtain qualitative information during the same chromatographic run, compared to MRM-only method. Thereafter, metabolic stability and metabolite identification of TJ0711 HCl were investigated using human liver microsomes (HLM) by the MRM-IDA-EPI method. The in vitro metabolic stability parameters were calculated and t(1/2), microsomal intrinsic clearance (CL(int)), as well as hepatic CL, were 13.0 min, 106.5 μL/min/mg microsomal protein, and 1082.2 mL/min, respectively. The major formed metabolites were also simultaneously monitored and the metabolite profiling data demonstrated that this MRM-IDA-EPI method was capable of targeting a large number of metabolites, in which demethylation and hydroxylation were the principle metabolism pathways during the in vitro incubation with HLM.

Determination of drug-like properties of a novel antileishmanial compound: In vitro absorption, distribution, metabolism, and excretion studies

In drug discovery research, the compounds should not only to be potent and selective but also must possess acceptable pharmacokinetic properties such as absorption, distribution, metabolism, and excretion (ADME) to increase success rate in clinical studies.

It is time for a paradigm shift in drug discovery bioanalysis: from SRM to HRMS

It can be argued that the last true paradigm shift in the bioanalytical (BA) arena was the shift from high-performance liquid chromatography (HPLC) with ultraviolet (UV) detection to HPLC with tandem mass spectrometry (MS/MS) detection after the commercialization of the triple quadrupole mass spectrometer in the 1990s. HPLC-MS/MS analysis based on selected reaction monitoring (SRM) has become the gold standard for BA assays and is used by all the major pharmaceutical companies for the quantitative analysis of new drug entities (NCEs) as part of the new drug discovery and development process. While LC-MS/MS continues to be the best tool for drug discovery bioanalysis, a new paradigm involving high-resolution mass spectrometry (HRMS) and ultrahigh-pressure liquid chromatography (uHPLC) is starting to make inroads into the pharmaceutical industry. The ability to collect full scan spectra, with excellent mass accuracy, mass resolution, 10-250 ms scan speeds and no NCE-related MS parameter optimization, makes the uHPLC-HRMS techniques suitable for quantitative analysis of NCEs while preserving maximum qualitative information about other drug-related and endogenous components such as metabolites, degradants, biomarkers and formulation materials. In this perspective article, we provide some insight into the evolution of the hybrid quadrupole-time-of-flight (Qq-TOF) mass spectrometer and propose some of the desirable specifications that such HRMS systems should have to be integrated into the drug discovery bioanalytical workflow for performing integrated qualitative and quantitative bioanalysis of drugs and related components.

Application of a rapid and integrated analysis system (RIAS) as a high-throughput processing tool for in vitro ADME samples by liquid chromatography/tandem mass spectrometry

Over the past decade, drug discovery programs have started to address the optimization of key ADME properties already at an early stage of the process. Hence, analytical chemists have been confronted with tremendously rising sample numbers and have had to develop methodologies accelerating quantitative liquid chromatography/tandem mass spectrometry (LC/MS/MS). This article focuses on the application of a generic and fully automated LC/MS/MS, named Rapid and Integrated Analysis System (RIAS), as a high-throughput platform for the rapid quantification of drug-like compounds in various in vitro ADME assays. Previous efforts were dedicated to the setup and feasibility study of a workflow-integrated platform combining a modified high-throughput liquid handling LC/MS/MS system controlled by a customized software interface and a customized data-processing and reporting tool. Herein the authors present an extension of this previously developed basic application to a broad set of ADME screening campaigns, covering CYP inhibition, Caco-2, and PAMPA assays. The platform is capable of switching automatically between various ADME assays, performs MS compound optimization if required, and provides a speed of 8 s from sample to sample, independently of the type of ADME assay. Quantification and peak review are adopted to the high-throughput environment and tested against a standard HPLC-ESI technology.

Structure-based design of potent and selective 2-(quinazolin-2-yl)phenol inhibitors of checkpoint kinase 2

Structure-based design was applied to the optimization of a series of 2-(quinazolin-2-yl)phenols to generate potent and selective ATP-competitive inhibitors of the DNA damage response signaling enzyme checkpoint kinase 2 (CHK2). Structure-activity relationships for multiple substituent positions were optimized separately and in combination leading to the 2-(quinazolin-2-yl)phenol 46 (IC(50) 3 nM) with good selectivity for CHK2 against CHK1 and a wider panel of kinases and with promising in vitro ADMET properties. Off-target activity at hERG ion channels shown by the core scaffold was successfully reduced by the addition of peripheral polar substitution. In addition to showing mechanistic inhibition of CHK2 in HT29 human colon cancer cells, a concentration dependent radioprotective effect in mouse thymocytes was demonstrated for the potent inhibitor 46 (CCT241533).

Development of QSAR models for microsomal stability: identification of good and bad structural features for rat, human and mouse microsomal stability

High throughput microsomal stability assays have been widely implemented in drug discovery and many companies have accumulated experimental measurements for thousands of compounds. Such datasets have been used to develop in silico models to predict metabolic stability and guide the selection of promising candidates for synthesis. This approach has proven most effective when selecting compounds from proposed virtual libraries prior to synthesis. However, these models are not easily interpretable at the structural level, and thus provide little insight to guide traditional synthetic efforts. We have developed global classification models of rat, mouse and human liver microsomal stability using in-house data. These models were built with FCFP_6 fingerprints using a Naïve Bayesian classifier within Pipeline Pilot. The test sets were correctly classified as stable or unstable with satisfying accuracies of 78, 77 and 75% for rat, human and mouse models, respectively. The prediction confidence was assigned using the Bayesian score to assess the applicability of the models. Using the resulting models, we developed a novel data mining strategy to identify structural features associated with good and bad microsomal stability. We also used this approach to identify structural features which are good for one species but bad for another. With these findings, the structure-metabolism relationships are likely to be understood faster and earlier in drug discovery.

The conduct of drug metabolism studies considered good practice (II): in vitro experiments

In vitro drug metabolism studies, which are inexpensive and readily carried out, serve as an adequate screening mechanism to characterize drug metabolites, elucidate their pathways, and make suggestions for further in vivo testing. This publication is a sequel to part I in a series and aims at providing a general framework to guide designs and protocols of the in vitro drug metabolism studies considered good practice in an efficient manner such that it would help researchers avoid common pitfalls and misleading results. The in vitro models include hepatic and non-hepatic microsomes, cDNA-expressed recombinant human CYPs expressed in insect cells or human B lymphoblastoid, chemical P450 inhibitors, S9 fraction, hepatocytes and liver slices. Important conditions for conducting the in vitro drug metabolism studies using these models are stated, including relevant concentrations of enzymes, co-factors, inhibitors and test drugs; time of incubation and sampling in order to establish kinetics of reactions; appropriate control settings, buffer selection and method validation. Separate in vitro data should be logically integrated to explain results from animal and human studies and to provide insights into the nature and consequences of in vivo drug metabolism. This article offers technical information and data and addresses scientific rationales and practical skills related to in vitro evaluation of drug metabolism to meet regulatory requirements for drug development.

An automated high throughput liquid chromatography-mass spectrometry process to assess the metabolic stability of drug candidates

An automated high throughput process, termed the MetFast assay, is described to assess in vitro the general microsomal cytochrome P450 beta-nicotinamide adenine dinucleotide phosphate-mediated first-pass metabolic stability of potential drug candidates as a utility for pharmaceutical profiling. Utilizing robotic protocols with a multiprobe liquid handler, compounds are incubated with liver microsomes from different species. Samples are then analyzed by in-line liquid chromatography (LC)-mass spectrometry (MS) to determine the amount of compound remaining after a certain time, which allows calculation of metabolism rates. To quantitatively assess large numbers of structurally diverse compounds by LC-MS, a strategy based on an iterative two-step process was devised. Initially compounds are qualitatively analyzed by LC-ultraviolet (UV)/MS (step 1) to determine purity (UV detection) and structural integrity (MS detection). This step ensures that only correct and verified compounds with sufficient purity are being assayed to obtain reproducible high data quality. In addition, all necessary information is gathered to automatically generate specific quantitative methods for the subsequent bioanalytical analysis of metabolic stability samples by LC-UV/MS (step 2). In-house-developed, highly flexible and sophisticated data management software, termed SmartReport, is utilized for automated qualitative and quantitative LC-MS analysis set-up, data processing, and results reporting. The integration of key aspects, inherent "universal" collision-induced dissociation settings of ion trap mass spectrometers for tandem mass spectrometric scan functions utilized for compound-specific and sensitive quantitative MS methods, generic fast-LC conditions, generic MS instrument settings, and the functionality of SmartReport software resulted in an analytical process that routinely provides reproducible high-quality metabolic stability data on structurally diverse compounds. Described here is the setup of the MetFast assay, and metabolic stability data from assay validation compounds are given.

Comparison of cytochrome P450 inhibition assays for drug discovery using human liver microsomes with LC–MS, rhCYP450 isozymes with fluorescence, and double cocktail with LC–MS

The disparity of IC(50)s from CYP450 inhibition assays used to assess drug-drug interaction potential was investigated, in order to have evidence for selecting a reliable in vitro CYP450 inhibition assay to support drug discovery. Three assays were studied: individual rhCYP isozymes and corresponding coumarin derivative-probe substrates with fluorescent detection, human liver microsomes (HLM) and cocktail drug-probe substrates with LC-MS detection, and double cocktail rhCYP isozymes mix and drug-probe mix with LC-MS detection. Data comparisons showed that the rhCYP-fluorescent assay and the cocktail assay with HLM-LC-MS had weak correlation. Detection method and probe substrates were shown to not be the major cause of the disparity in IC(50)s. However, the enzyme source and composition (HLM versus, rhCYP) caused disparity in IC(50)s. Specifically, the high concentrations of CYP isozymes often used with HLM-based assays produced high probe substrate conversion and test compound metabolism, which should both contribute to artificially higher IC(50)s. Non-specific binding of substrate to higher concentration proteins and lipids in the HLM-based assays should also contribute to higher IC(50)s. The modified double cocktail assay was found to overcome limitations of the other two assays. It uses an rhCYP isozymes mix, drug-probe substrate mix, low protein concentration, and LC-MS detection. The double cocktail assay is sensitive, selective, and high throughout for use in drug discovery to provide an early alert to potential toxicity with regard to drug-drug interaction, prioritize chemical series, and guide structural modification to circumvent CYP450 inhibition.

Methods for Predicting Human Drug Metabolism

Drug metabolism information is a necessary component of drug discovery and development. The key issues in drug metabolism include identifying: the enzyme(s) involved, the site(s) of metabolism, the resulting metabolite(s), and the rate of metabolism. Methods for predicting human drug metabolism from in vitro and computational methodologies and determining relationships between the structure and metabolic activity of molecules are also critically important for understanding potential drug interactions and toxicity. There are numerous experimental and computational approaches that have been developed in order to predict human metabolism which have their own limitations. It is apparent that few of the computational tools for metabolism prediction alone provide the major integrated functions needed to assist in drug discovery. Similarly the different in vitro methods for human drug metabolism themselves have implicit limitations. The utilization of these methods for pharmaceutical and other applications as well as their integration is discussed as it is likely that hybrid methods will provide the most success.

High throughput microsomal stability assay for insoluble compounds

High throughput metabolic stability assays are widely implemented in drug discovery to guide structural modification, predict in vivo performance, develop structure-metabolic stability relationships, and triage compounds for in vivo animal studies. However, these methods are often developed and validated using commercial drugs. Many drug discovery compounds differ from commercial drugs, with many having high lipophilicity, high molecular weight and low solubility. The impact of very low solubility on metabolic stability assay results was explored. Two metabolic stability assays, the 'aqueous dilution method' and the 'cosolvent method, were compared. For commercial drugs and most discovery compounds having reasonable drug-like properties, the two methods gave comparable results. For highly lipophilic, insoluble drug discovery compounds, the 'aqueous dilution method' gave artificially higher stability results. The cosolvent method performs compound dilutions in solutions with higher organic solvent content and adds solutions directly to microsomes to assist with solubilization, minimize precipitation and reduce non-specific binding to plastics. This method is more applicable in drug discovery where compounds of a wide range of solubility are studied.

Principles and applications of LC-MS in new drug discovery

The use of high-performance liquid chromatography combined with mass spectrometry (HPLC-MS) or tandem mass spectrometry (HPLC-MS-MS) has proven to be the analytical technique of choice for most assays used in various stages of new drug discovery. A summary of the key components of HPLC-MS systems, as well as an overview of major application areas that use this technique as part of the drug discovery process, will be described here. This review will also provide an introduction into the various types of mass spectrometers that can be selected for the multiple tasks that can be performed using LC-MS as the analytical tool. The strategies for optimizing the use of this technique and also the potential problems and how to avoid them will be highlighted.

Investigation of the utility of published in vitro intrinsic clearance data for prediction of in vivo clearance

INTRODUCTION

This study was conducted to compare and contrast published in vitro intrinsic clearance values reported for compounds from different laboratories and the predictivity of these data to project in vivo clearance.

METHODS

A total of 103 compounds were selected for investigation and an exhaustive literature search was conducted to identify in vitro intrinsic clearance (CL,i) values for comparative purposes. The simple well-stirred model was used to predict in vivo clearance using these in vitro intrinsic clearance values.

RESULTS

Data were available in the literature for <10% of the compounds of interest in rat, dog, monkey, or human S9, as well as <10% for dog or monkey microsomes or hepatocytes. Therefore, this comparative exercise was limited to rat and human microsomes and hepatocytes. Examination of the available CL,i values indicated a substantial (up to 100 s-fold) variation in values reported in the literature; this variability translated into substantial variation in predicted in vivo clearance.

DISCUSSION

The literature paucity and variability described here demonstrate the importance of generating experimentally consistent de novo CL,i data for the purpose of method validation or in vitro-in vivo scaling.

Recent advances in pharmacokinetic extrapolation from preclinical data to humans

The early characterisation of drug metabolism and pharmacokinetic (DMPK) properties of new chemical entities plays a key role in the pharmaceutical industry's effort to reduce attrition. Specifically, a major goal of early DMPK studies is to accurately predict the behaviour of new chemical entities in humans, thus allowing likely failures to be terminated rapidly and resource to be placed on molecules most likely to succeed. The present review summarises progress over the past several years in the key technologies used in the pharmaceutical industry to achieve these goals: namely, in vivo, in vitro and in silico/computational tools. The limitations of the various assays are discussed, with attention also given to likely future directions in this field.

High throughput P450 inhibition screens in early drug discovery

This review of high throughput (HT) P450 inhibition technologies and their impact on early drug discovery finds the field at a mature stage. The relationship between P450 inhibition and drug-drug interactions is well understood. A wide variety of P450 inhibition detection technologies are readily available off-the-shelf, but what seems still to be missing is a general agreement on how much weight one should give to the various types of early discovery HT P450 inhibition data. Method-dependent potency differences are a cause of concern, and to resolve this issue the authors advocate calibration of the HT methods with a large set of marketed drugs.

Development and Application of an Automated Solution Stability Assay for Drug Discovery

Screening of solution stability provides an early alert on potential liabilities of drug candidates so that strategies can be developed to overcome the challenges. Afully automated solution stability assay has been developed to accelerate traditionalmanual operation. The assay uses the advanced capabilities of a high-performance liquid chromatography instrument that is present in many pharmaceutical research laboratories. The samples are prepared automatically by a temperature-controlled autosampler. The samples are delivered to the stability matrices, mixed, incubated, and injected at selected time points during the reaction time course. This automated process occurs without operator intervention, thus allowing 96 experiments to be run with0.5hof a scientist's time compared to 8 h for the same studywhenperformedmanually. Automationnotonly eliminates themanual operation but also improves accuracy and throughput. The assay protocol has been optimized to achieve homogenous mixing and eliminate carryover. The assay is robust, flexible, and high throughput. It can be used to study stability for a large number of samples undermultiple incubation conditions and has awide range of applications in drug discovery and development, such as screening compound stability in biological assaymedia, obtaining a stability-pH profile, surveying compound stability in physiological fluids, and performing development forced degradation and excipient compatibility.

A novel approach to perform metabolite screening during the quantitative LC-MS/MS analyses of in vitro metabolic stability samples using a hybrid triple-quadrupole linear ion trap mass spectrometer

In vitro metabolic stability experiments using microsomes or other liver preparations are important components in the discovery and lead-optimization stages of compound selection in the pharmaceutical industry. Currently, liquid chromatography-tandem mass spectrometric (LC-MS/MS) support of in vitro metabolic stability studies primarily involves the monitoring of disappearance of parent compounds, using selected reaction monitoring (SRM) on triple-quadrupole instruments. If moderate to high turnover is observed, separate metabolite identification experiments are then conducted to characterize the biotransformation products. In this paper, we present a novel method to simultaneously perform metabolite screening in addition to the quantitative stability measurements, both within the same chromatographic run. This is accomplished by combining SRM and SRM-triggered, information-dependent acquisition (IDA) of MS/MS spectra on a hybrid triple-quadrupole linear ion trap (QqQLIT) mass spectrometer. Microsomal stability experiments using model compounds, bufuralol, propranolol, imipramine, midazolam, verapamil and diclofenac, were used to demonstrate the applicability of our approach. This SRM + SRM-IDA approach generated metabolic stability results similar to those obtained by conventional SRM-only approach. In addition, MS/MS spectra from potential metabolites were obtained with the enhanced product ion (EPI) scan function of LIT during the same injection. These spectra were correlated to the spectra of parent compounds to confirm the postulated structures. The time-concentration profiles of identified metabolites were also estimated from the acquired data. This approach has been successfully used to support discovery programs.

Development and application of high throughput plasma stability assay for drug discovery

Plasma stability plays an important role in drug discovery and development. Unstable compounds tend to have rapid clearance and short half-life, resulting in poor in vivo performance. This paper examines the variables that affect the plasma stability assay results, including substrate concentration, %DMSO, plasma concentration, enzyme activity upon incubation and batch variation. The results show that plasma stability can accommodate a wide range of experimental conditions. Relatively minor differences in results are produced with major differences in conditions. Significant batch-to-batch variations were observed for rat plasma. We selected the following conditions: 1 microM substrate concentration, 2.5% DMSO, and 50% dilution of plasma in pH 7.4 buffer. Plasma stability can be used as a diagnostic assay when compounds are unexpectedly rapidly cleared, as a special assay when structural classes contain groups that may be susceptible to plasma enzyme hydrolysis, or as general screen for compounds if resources are available. Plasma stability assay has many applications in drug discovery: to alert teams to labile structural motifs, to prioritize compounds for in vivo studies and to screen prodrugs and antedrugs.

Validation of a high-throughput absorption, distribution, metabolism, and excretion (ADME) system and results for 60 literature compounds

A high-throughput analytical system for the support of absorption, distribution, metabolism, and excretion (ADME) was constructed in collaboration with Gubbs Inc. to commercialize corresponding software. We sought to quickly build and validate this system for microsomal clearance assessment using 60 commercially available non-proprietary compounds that are non-DEA-restricted in addition to 36 proprietary Millennium compounds that had already been assessed using a low-throughput infrastructure. The system was constructed such that a approximately 45 second total cycle time was achieved injection-to-injection. Software was successfully coded to enable the analyst to submit multiple batches, and modify multiple methods very quickly for use with Applied Biosystems Analyst 1.3. After acquisition the software was used to simultaneously integrate multiple injection chromatograms, regress the data, and calculate clearance such that all of the data could be easily and immediately reviewed by both bioanalytical and enzymology personnel. Unfortunately, despite an exhaustive search of the literature, we were unable to find a large number of non-proprietary compound data for validation, so we provide such a source of data here. Results are presented for the 60 literature compounds that were assessed. A good correlation was observed between literature results for 16 compounds that we were able to find and the results obtained using the system. The Millennium proprietary compounds that we assessed using both low- and high-throughput approaches also correlated well. We present here a system for the support of high-throughput in vitro ADME analysis and also present the results of 60 non-proprietary, non-DEA-scheduled compounds to facilitate the validation efforts of others. Finally, we present commercially available software to facilitate high-throughput ADME systems in the community.

Experimental Design on Single-Time-Point High-Throughput Microsomal Stability Assay

An experimental design for a single-time-point microsomal stability assay was evaluated as compared with multiple-time-point studies. Results obtained from single-time-point experiments are in excellent agreement with those from multiple time points. First-order reaction kinetics revealed rapid changes of predicted half-life from percent remaining of the parent compound at the inflection points, suggesting a maximum predictive limit for half-life. Selection of the incubation time in single-time-point assays is important to obtain balanced information for stable and unstable compounds. A short incubation time (e.g., 5 min) is most useful for differentiating between unstable compounds, which is beneficial to direct the synthetic efforts in projects with poor metabolic stability. A long incubation time (e.g., 30 min) is more applicable to a compound series with high metabolic stability. For screening purposes, a moderate incubation time (e.g., 15 min) is recommended to achieve good resolution and a sufficiently high maximum predictive limit for half-life. This study suggests that a single-time-point assay is sufficient for ranking compounds in early drug discovery. It increases throughput and reduces turnaround time and cost.