Towards a new age of virtual ADME/TOX and multidimensional drug discovery

In silico ADME/tox comes of age: twenty years later

In the early 2000s pharmaceutical drug discovery was beginning to use computational approaches for absorption, distribution, metabolism, excretion and toxicity (ADME/Tox, also known as ADMET) prediction. This emphasis on prediction was an effort to reduce the risk of later stage failures from ADME/Tox.Much has been written in the intervening twenty plus years and significant expenditure has occurred in companies developing these in silico capabilities which can be gleaned from publications. It is therefore an appropriate time to briefly reflect on what was proposed then and what the reality is today.20 years ago, we tended to optimise bioactivity and perhaps one ADME/Tox property at a time. Previously pharmaceutical companies needed a whole infrastructure for models - in silico and in vitro experts, IT, champions on a project team, educators and management support. Now we are in the age of generative de novo design where bioactivity and many ADME/Tox properties can be optimised and large language model technologies are available.There are also some challenges such as the focus on very large molecules which may be outside of current ADME/Tox models.We provide an opportunity to look forward with the increasing public data for ADME/Tox as well as expanded types of algorithms available.

Isolation of quinic acid from dropped Citrus reticulata Blanco fruits: its derivatization, antibacterial potential, docking studies, and ADMET profiling

Citrus reticulata dropped fruits are generally discarded as waste, causing environmental pollution and losses to farmers. In the present study, column chromatography has been used to isolate quinic acid (1,3,4,5-tetrahydroxycyclohexane-1-carboxylic acid) from the ethyl acetate fraction of a methanol extract of citrus fruits dropped in April. Quinic acid is a ubiquitous plant metabolite found in various plants and microorganisms. It is an important precursor in the biosynthesis of aromatic natural compounds. It was further derivatized into 3,4-o-isopropylidenequinic acid 1,5-lactone (QA1), 1,3,4,5-tetraacetoxycyclohexylaceticanhydride (QA2), and cyclohexane-1,2,3,5-tetraone (QA3). These compounds were further tested for their antibacterial potential against the foodborne pathogens Staphylococcus aureus, Bacillus spp., Yersinia enterocolitica, and Escherichia coli. QA1 exhibited maximum antibacterial potential (minimum inhibitory concentration; 80-120 μg/mL). QA1 revealed synergistic behavior with streptomycin against all the tested bacterial strains having a fractional inhibitory concentration index ranging from 0.29 to 0.37. It also caused a significant increase in cell constituent release in all the tested bacteria compared to the control, along with prominent biofilm reduction. The results obtained were further checked with computational studies that revealed the best docking score of QA1 (-6.30 kcal/mol, -5.8 kcal/mol, and -4.70 kcal/mol) against β-lactamase, DNA gyrase, and transpeptidase, respectively. The absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis revealed that the drug-like properties of QA1 had an ideal toxicity profile, making it a suitable candidate for the development of antimicrobial drugs.

Synergistic potential of Citrus aurantium L. essential oil with antibiotics against Candida albicans

ETHNOPHARMACOLOGICAL RELEVANCE

Citrus aurantium L. is used in traditional medicine in India for treating stomach ache, vomiting, blood pressure, dysentery, diarrhea, cardiovascular analeptic, sedative, boils and urinary tract infections. Its essential oil from fruit peels has antioxidant, antimicrobial, antifungal, antiparasitic, and anti-inflammatory activities.

AIM OF THE STUDY

The aim of the study was to characterize the antifungal activity and synergistic potential of essential oil extracted from leaves of Citrus aurantium L. of North-Western Himalayas against Candida albicans.

MATERIALS AND METHODS

Citrus aurantium essential oil (CAEO) was extracted from leaves and characterized by GC-MS. The antifungal activity and synergistic potential of CAEO against C. albicans was studied by agar well diffusion, and broth microdilution assay. The anti-fungal potential of the phytoconstituents of CAEO was studied by in silico interaction with two fungal drug targets, N-myristoyl transferase (NMT) and Cytochrome P450 14 Alpha-sterol Demethylase (CYP51).

RESULTS

CAEO exhibited strong antifungal activity against two strains of C. albicans, with fungicidal effect. The MIC of CAEO against C. albicans strains was 0.15 - 0.31% (v/v). CAEO exhibited synergistic potential with fluconazole and amphotericin B against C. albicans and enhanced the antifungal efficacy of the clinical drugs by 8.3 to 34.4 folds. The GC-MS analysis of CAEO identified at least ten compounds, with 2-β pinene, δ-3 Carene and D-limonene as the major compounds. In silico molecular docking of the three major phytocompounds of CAEO with NMT and CYP51 revealed their potential to interact with both targets. δ-3 Carene showed best binding (E_total of -131.13 kcal/mol) with NMT, while D-limonene exhibited highest binding energy (E_total of -175.23 kcal/mol) with CYP51. ADME/T analysis showed that 2-β pinene, δ-3 Carene and D-limonene exhibit drug likeliness and ideal toxicity profiles for their use as drug candidates.

CONCLUSIONS

Thus, the essential oil from leaves of C. aurantium and its phytocomponents can be used as sustainable and natural therapeutic to treat candidiasis as well as a resource to enhance the potency of clinical antibiotics, which have lost efficacy due to emergence of drug resistance in C. albicans.

Computer-Aided Drug Design Methods

Computational approaches are useful tools to interpret and guide experiments to expedite the antibiotic drug design process. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two general types of computer-aided drug design (CADD) approaches in existence. SBDD methods analyze macromolecular target 3-dimensional structural information, typically of proteins or RNA, to identify key sites and interactions that are important for their respective biological functions. Such information can then be utilized to design antibiotic drugs that can compete with essential interactions involving the target and thus interrupt the biological pathways essential for survival of the microorganism(s). LBDD methods focus on known antibiotic ligands for a target to establish a relationship between their physiochemical properties and antibiotic activities, referred to as a structure-activity relationship (SAR), information that can be used for optimization of known drugs or guide the design of new drugs with improved activity. In this chapter, standard CADD protocols for both SBDD and LBDD will be presented with a special focus on methodologies and targets routinely studied in our laboratory for antibiotic drug discoveries.

The application of in silico drug-likeness predictions in pharmaceutical research

The concept of drug-likeness, established from the analyses of the physiochemical properties or/and structural features of existing small organic drugs or/and drug candidates, has been widely used to filter out compounds with undesirable properties, especially poor ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles. Here, we summarize various approaches for drug-likeness evaluations, including simple rules/filters based on molecular properties/structures and quantitative prediction models based on sophisticated machine learning methods, and provide a comprehensive review of recent advances in this field. Moreover, the strengths and weaknesses of these approaches are briefly outlined. Finally, the drug-likeness analyses of natural products and traditional Chinese medicines (TCM) are discussed.

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

On the order of hundreds of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) models have been described in the literature in the past decade which are more often than not inaccessible to anyone but their authors. Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery. We describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Kit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build an array of Bayesian models for ADME/Tox, in vitro and in vivo bioactivity, and other physicochemical properties. We show that these models possess cross-validation receiver operator curve values comparable to those generated previously in prior publications using alternative tools. We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the user’s own datasets. The current study sets the stage for generating models in proprietary software (such as CDD) and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery.

Computer based screening for novel inhibitors against Vibrio cholerae using NCI diversity set-II: an alternative approach by targeting transcriptional activator ToxT

Cholera is a severe diarrheal disease caused by Vibrio cholerae and remains as a major health risk in developing countries. The emergence and spread of multi-drug resistant V. cholerae strains during the past two decades is now a major problem in the treatment of cholera and have created the urgent need for the development of novel therapeutic agents. Targeting transcriptional factor is now a novel approach to tackle the development of multi-drug resistant strain. In the recent year virtual high throughput screening has emerged as a widely accepted powerful technology in the identification of novel and diverse lead. This study provides new insight to the search for new potent and selective inhibitors that still remains necessary to avoid the risk of possible resistance and reduce toxicity and side effects of currently available cholera drugs. The publications of high resolution X-ray structure of V. cholerae ToxT has open the way to the structure based virtual screening to identify new small molecular inhibitors which still remain necessary to avoid the risk of possible resistance and reduce toxicity and side effects of currently available cholera drugs. In this study we have performed structure based virtual screening approach using NCI diversity set-II to look for novel inhibitor of ToxT and proposed eight candidate compounds with high scoring function. Thus from complex scoring and binding ability it is elucidated that these compounds could be the promising inhibitors or could be developed as novel lead compounds for drug design against cholera.

Advances in multiparameter optimization methods for de novo drug design

INTRODUCTION

A high-quality drug must achieve a balance of physicochemical and absorption, distribution, metabolism and elimination properties, safety and potency against its therapeutic target(s). Multiparameter optimization (MPO) methods guide the simultaneous optimization of multiple factors to quickly target compounds with the highest chance of downstream success. MPO can be combined with 'de novo design' methods to automatically generate and assess a large number of diverse structures and identify strategies to optimize a compound's overall balance of properties.

AREAS COVERED

The article provides a review of MPO methods and recent developments in the methods and opinions in the field. It also provides a description of advances in de novo design that improve the relevance of automatically generated compound structures and integrate MPO. Finally, the article provides discussion of a recent case study of the automatic design of ligands to polypharmacological profiles.

EXPERT OPINION

Recent developments have reduced the generation of chemically infeasible structures and improved the quality of compounds generated by de novo design methods. There are concerns about the ability of simple drug-like properties and ligand efficiency indices to effectively guide the detailed optimization of compounds. De novo design methods cannot identify a perfect compound for synthesis, but it can identify high-quality ideas for detailed consideration by an expert scientist.

Progress in computational toxicology

INTRODUCTION

Computational methods have been widely applied to toxicology across pharmaceutical, consumer product and environmental fields over the past decade. Progress in computational toxicology is now reviewed.

METHODS

A literature review was performed on computational models for hepatotoxicity (e.g. for drug-induced liver injury (DILI)), cardiotoxicity, renal toxicity and genotoxicity. In addition various publications have been highlighted that use machine learning methods. Several computational toxicology model datasets from past publications were used to compare Bayesian and Support Vector Machine (SVM) learning methods.

RESULTS

The increasing amounts of data for defined toxicology endpoints have enabled machine learning models that have been increasingly used for predictions. It is shown that across many different models Bayesian and SVM perform similarly based on cross validation data.

DISCUSSION

Considerable progress has been made in computational toxicology in a decade in both model development and availability of larger scale or 'big data' models. The future efforts in toxicology data generation will likely provide us with hundreds of thousands of compounds that are readily accessible for machine learning models. These models will cover relevant chemistry space for pharmaceutical, consumer product and environmental applications.

Classification models for safe drug molecules

Frequent failure of drug candidates during development stages remains the major deterrent for an early introduction of new drug molecules. The drug toxicity is the major cause of expensive late-stage development failures. An early identification/optimization of the most favorable molecule will naturally save considerable cost, time, human efforts and minimize animal sacrifice. (Quantitative) Structure Activity Relationships [(Q)SARs] represent statistically derived predictive models correlating biological activity (including desirable therapeutic effect and undesirable side effects) of chemicals (drugs/toxicants/environmental pollutants) with molecular descriptors and/or properties. (Q)SAR models which categorize the available data into two or more groups/classes are known as classification models. Numerous techniques of diverse nature are being presently employed for development of classification models. Though there is an increasing use of classification models for prediction of either biological activity or toxicity, the future trend will naturally be towards the development of classification models capable of simultaneous prediction of biological activity, toxicity, and pharmacokinetic parameters so as to accelerate development of bioavailable safe drug molecules.

PaDEL-DDPredictor: open-source software for PD-PK-T prediction

ADMET (absorption, distribution, metabolism, excretion, and toxicity)-related failure of drug candidates is a major issue for the pharmaceutical industry today. Prediction of PD-PK-T properties using in silico tools has become very important in pharmaceutical research to reduce cost and enhance efficiency. PaDEL-DDPredictor is an in silico tool for rapid prediction of PD-PK-T properties of compounds from their chemical structures. It is free and open-source software that, has both graphical user interface and command line interface, can work on all major platforms (Windows, Linux, and MacOS) and supports more than 90 different molecular file formats. The software can be downloaded from http://padel.nus.edu.sg/software/padelddpredictor.

Prediction of pharmacokinetic parameters

In silico tools specifically developed for prediction of pharmacokinetic parameters are of particular interest to pharmaceutical industry because of the high potential of discarding inappropriate molecules during an early stage of drug development itself with consequent saving of vital resources and valuable time. The ultimate goal of the in silico models of absorption, distribution, metabolism, and excretion (ADME) properties is the accurate prediction of the in vivo pharmacokinetics of a potential drug molecule in man, whilst it exists only as a virtual structure. Various types of in silico models developed for successful prediction of the ADME parameters like oral absorption, bioavailability, plasma protein binding, tissue distribution, clearance, half-life, etc. have been briefly described in this chapter.

Reconstructing human exposures using biomarkers and other "clues"

Biomonitoring is the process by which biomarkers are measured in human tissues and specimens to evaluate exposures. Given the growing number of population-based biomonitoring surveys, there is now an escalated interest in using biomarker data to reconstruct exposures for supporting risk assessment and risk management. While detection of biomarkers is de facto evidence of exposure and absorption, biomarker data cannot be used to reconstruct exposure unless other information is available to establish the external exposure-biomarker concentration relationship. In this review, the process of using biomarker data and other information to reconstruct human exposures is examined. Information that is essential to the exposure reconstruction process includes (1) the type of biomarker based on its origin (e.g., endogenous vs. exogenous), (2) the purpose/design of the biomonitoring study (e.g., occupational monitoring), (3) exposure information (including product/chemical use scenarios and reasons for expected contact, the physicochemical properties of the chemical and nature of the residues, and likely exposure scenarios), and (4) an understanding of the biological system and mechanisms of clearance. This review also presents the use of exposure modeling, pharmacokinetic modeling, and molecular modeling to assist in integrating these various types of information.

Accessing, using, and creating chemical property databases for computational toxicology modeling

Toxicity data is expensive to generate, is increasingly seen as precompetitive, and is frequently used for the generation of computational models in a discipline known as computational toxicology. Repositories of chemical property data are valuable for supporting computational toxicologists by providing access to data regarding potential toxicity issues with compounds as well as for the purpose of building structure-toxicity relationships and associated prediction models. These relationships use mathematical, statistical, and modeling computational approaches and can be used to understand the mechanisms by which chemicals cause harm and, ultimately, enable prediction of adverse effects of these chemicals to human health and/or the environment. Such approaches are of value as they offer an opportunity to prioritize chemicals for testing. An increasing amount of data used by computational toxicologists is being published into the public domain and, in parallel, there is a greater availability of Open Source software for the generation of computational models. This chapter provides an overview of the types of data and software available and how these may be used to produce predictive toxicology models for the community.

Applying medicinal chemistry transformations and multiparameter optimization to guide the search for high-quality leads and candidates

In this article we describe a computational method that automatically generates chemically relevant compound ideas from an initial molecule, closely integrated with in silico models, and a probabilistic scoring algorithm to highlight the compound ideas most likely to satisfy a user-defined profile of required properties. The new compound ideas are generated using medicinal chemistry 'transformation rules' taken from examples in the literature. We demonstrate that the set of 206 transformations employed is generally applicable, produces a wide range of new compounds, and is representative of the types of modifications previously made to move from lead-like to drug-like compounds. Furthermore, we show that more than 94% of the compounds generated by transformation of typical drug-like molecules are acceptable to experienced medicinal chemists. Finally, we illustrate an application of our approach to the lead that ultimately led to the discovery of duloxetine, a marketed serotonin reuptake inhibitor.

Molecular library design using multi-objective optimization methods

Advancements in combinatorial chemistry and high-throughput screening technology have enabled the synthesis and screening of large molecular libraries for the purposes of drug discovery. Contrary to initial expectations, the increase in screening library size, typically combined with an emphasis on compound structural diversity, did not result in a comparable increase in the number of promising hits found. In an effort to improve the likelihood of discovering hits with greater optimization potential, more recent approaches attempt to incorporate additional knowledge to the library design process to effectively guide the search. Multi-objective optimization methods capable of taking into account several chemical and biological criteria have been used to design collections of compounds satisfying simultaneously multiple pharmaceutically relevant objectives. In this chapter, we present our efforts to implement a multi-objective optimization method, MEGALib, custom-designed to the library design problem. The method exploits existing knowledge, e.g. from previous biological screening experiments, to identify and profile molecular fragments used subsequently to design compounds compromising the various objectives.

Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis

There is an urgent need for new drugs against tuberculosis which annually claims 1.7-1.8 million lives. One approach to identify potential leads is to screen in vitro small molecules against Mycobacterium tuberculosis (Mtb). Until recently there was no central repository to collect information on compounds screened. Consequently, it has been difficult to analyze molecular properties of compounds that inhibit the growth of Mtb in vitro. We have collected data from publically available sources on over 300 000 small molecules deposited in the Collaborative Drug Discovery TB Database. A cheminformatics analysis on these compounds indicates that inhibitors of the growth of Mtb have statistically higher mean logP, rule of 5 alerts, while also having lower HBD count, atom count and lower PSA (ChemAxon descriptors), compared to compounds that are classed as inactive. Additionally, Bayesian models for selecting Mtb active compounds were evaluated with over 100 000 compounds and, they demonstrated 10 fold enrichment over random for the top ranked 600 compounds. This represents a promising approach for finding compounds active against Mtb in whole cells screened under the same in vitro conditions. Various sets of Mtb hit molecules were also examined by various filtering rules used widely in the pharmaceutical industry to identify compounds with potentially reactive moieties. We found differences between the number of compounds flagged by these rules in Mtb datasets, malaria hits, FDA approved drugs and antibiotics. Combining these approaches may enable selection of compounds with increased probability of inhibition of whole cell Mtb activity.

Prediction of human pharmacokinetics—evaluation of methods for prediction of hepatic metabolic clearance

Methods for prediction of hepatic clearance (CLH) in man have been evaluated. A physiologically-based in-vitro to in-vivo (PB-IVIV) method with human unbound fraction in blood (fu,bl) and hepatocyte intrinsic clearance (CLint)-data has a good rationale and appears to give the best predictions (maximum ∼2-fold errors; < 25% errors for half of CL-predictions; appropriate ranking). Inclusion of an empirical scaling factor is, however, needed, and reasons include the use of cryopreserved hepatocytes with low activity, and inappropriate CLint- and fu,bl-estimation methods. Thus, an improvement of this methodology is possible and required. Neglect of fu,bl or incorporation of incubation binding does not seem appropriate. When microsome CLint-data are used with this approach, the CLH is underpredicted by 5- to 9-fold on average, and a 106-fold underprediction (attrition potential) has been observed. The poor performance could probably be related to permeation, binding and low metabolic activity. Inclusion of scaling factors and neglect of fu,bl for basic and neutral compounds improve microsome predictions. The performance is, however, still not satisfactory. Allometry incorrectly assumes that the determinants for CLH relate to body weight and overpredicts human liver blood flow rate. Consequently, allometric methods have poor predictability. Simple allometry has an average overprediction potential, > 2-fold errors for ∼1/3 of predictions, and 140-fold underprediction to 5800-fold overprediction (potential safety risk) range. In-silico methodologies are available, but these need further development. Acceptable prediction errors for compounds with low and high CLH should be ∼50 and ∼10%, respectively. In conclusion, it is recommended that PB-IVIV with human hepatocyte CLint and fu,bl is applied and improved, limits for acceptable errors are decreased, and that animal CLH-studies and allometry are avoided.

Knowledge-based approach to de novo design using reaction vectors

A knowledge-based approach to the de novo design of synthetically feasible molecules is described. The method is based on reaction vectors which represent the structural changes that take place at the reaction center along with the environment in which the reaction occurs. The reaction vectors are derived automatically from a database of reactions which is not restricted by size or reaction complexity. A structure generation algorithm has been developed whereby reaction vectors can be applied to previously unseen starting materials in order to suggest novel syntheses. The approach has been implemented in KNIME and is validated by reproducing known synthetic routes. We then present applications of the method in different drug design scenarios including lead optimization and library enumeration. The method offers great potential for capturing and using the growing body of data on reactions that is becoming available through electronic laboratory notebooks.

De novo drug design using multiobjective evolutionary graphs

Drug discovery and development is a complex, lengthy process, and failure of a candidate molecule can occur as a result of a combination of reasons, such as poor pharmacokinetics, lack of efficacy, or toxicity. Successful drug candidates necessarily represent a compromise between the numerous, sometimes competing objectives so that the benefits to patients outweigh potential drawbacks and risks. De novo drug design involves searching an immense space of feasible, druglike molecules to select those with the highest chances of becoming drugs using computational technology. Traditionally, de novo design has focused on designing molecules satisfying a single objective, such as similarity to a known ligand or an interaction score, and ignored the presence of the multiple objectives required for druglike behavior. Recently, methods have appeared in the literature that attempt to design molecules satisfying multiple predefined objectives and thereby produce candidate solutions with a higher chance of serving as viable drug leads. This paper describes the Multiobjective Evolutionary Graph Algorithm (MEGA), a new multiobjective optimization de novo design algorithmic framework that can be used to design structurally diverse molecules satisfying one or more objectives. The algorithm combines evolutionary techniques with graph-theory to directly manipulate graphs and perform an efficient global search for promising solutions. In the Experimental Section we present results from the application of MEGA for designing molecules that selectively bind to a known pharmaceutical target using the ChillScore interaction score family. The primary constraints applied to the design are based on the identified structure of the protein target and a known ligand currently marketed as a drug. A detailed explanation of the key elements of the specific implementation of the algorithm is given, including the methods for obtaining molecular building blocks, evolving the chemical graphs, and scoring the designed molecules. Our findings demonstrate that MEGA can produce structurally diverse candidate molecules representing a wide range of compromises of the supplied constraints and thus can be used as an "idea generator" to support expert chemists assigned with the task of molecular design.

Desirability-based multiobjective optimization for global QSAR studies: application to the design of novel NSAIDs with improved analgesic, antiinflammatory, and ulcerogenic profiles

Up to now, very few reports have been published concerning the application of multiobjective optimization (MOOP) techniques to quantitative structure-activity relationship (QSAR) studies. However, none reports the optimization of objectives related directly to the desired pharmaceutical profile of the drug. In this work, for the first time, it is proposed a MOOP method based on Derringer's desirability function that allows conducting global QSAR studies considering simultaneously the pharmacological, pharmacokinetic and toxicological profile of a set of molecule candidates. The usefulness of the method is demonstrated by applying it to the simultaneous optimization of the analgesic, antiinflammatory, and ulcerogenic properties of a library of fifteen 3-(3-methylphenyl)-2-substituted amino-3H-quinazolin-4-one compounds. The levels of the predictor variables producing concurrently the best possible compromise between these properties is found and used to design a set of new optimized drug candidates. Our results also suggest the relevant role of the bulkiness of alkyl substituents on the C-2 position of the quinazoline ring over the ulcerogenic properties for this family of compounds. Finally, and most importantly, the desirability-based MOOP method proposed is a valuable tool and shall aid in the future rational design of novel successful drugs.

Can in vitro assessment provide relevant end points for cognitive drug programs?

Several start-up biotechnology companies have been created with the primary intent of developing cognitive enhancers. In addition, established pharmaceutical companies also frequently focus their efforts on cognitive drug discovery. In many instances, the rationale and evidence for these endeavors are based largely on in vitro assessments. In particular, the experimental paradigm, know as long-term potentiation (LTP), a cellular model of synaptic plasticity and memory encoding, is being increasing used preclinically for assessing potential nootropic drugs in vitro. Central to this thinking is the idea that the modulation of LTP and/or glutamate receptors are the key criteria that must be met for the development of cognitive enhancers. However, programs targeting the NMDA receptor, a glutamate receptor subtype, over the years have been less than fruitful. In addition, skeptics criticize the relevance of some in vitro tests such as LTP for simulating human cognitive function. Given these considerations, one may wonder if in vitro assessments in general, and the LTP paradigm in particular, provide relevant end points for cognitive drug discovery and development programs. The focus of this article is to address this question and to present evidence as to why in vitro assessment is still critical to the success of any cognitive drug program.

Desirability-based methods of multiobjective optimization and ranking for global QSAR studies. Filtering safe and potent drug candidates from combinatorial libraries

Up to now, very few applications of multiobjective optimization (MOOP) techniques to quantitative structure-activity relationship (QSAR) studies have been reported in the literature. However, none of them report the optimization of objectives related directly to the final pharmaceutical profile of a drug. In this paper, a MOOP method based on Derringer's desirability function that allows conducting global QSAR studies, simultaneously considering the potency, bioavailability, and safety of a set of drug candidates, is introduced. The results of the desirability-based MOOP (the levels of the predictor variables concurrently producing the best possible compromise between the properties determining an optimal drug candidate) are used for the implementation of a ranking method that is also based on the application of desirability functions. This method allows ranking drug candidates with unknown pharmaceutical properties from combinatorial libraries according to the degree of similarity with the previously determined optimal candidate. Application of this method will make it possible to filter the most promising drug candidates of a library (the best-ranked candidates), which should have the best pharmaceutical profile (the best compromise between potency, safety and bioavailability). In addition, a validation method of the ranking process, as well as a quantitative measure of the quality of a ranking, the ranking quality index (Psi), is proposed. The usefulness of the desirability-based methods of MOOP and ranking is demonstrated by its application to a library of 95 fluoroquinolones, reporting their gram-negative antibacterial activity and mammalian cell cytotoxicity. Finally, the combined use of the desirability-based methods of MOOP and ranking proposed here seems to be a valuable tool for rational drug discovery and development.

In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.

Molecular modeling approaches for the prediction of the nonspecific binding of drugs to hepatic microsomes

Molecular modeling approaches for the prediction of the nonspecific binding of drugs to hepatic microsomes were examined using a published database of 56 compounds. Models generated were evaluated using an independent test set of 13 compounds. A pharmacophore approach identified structural features of drugs associated with nonspecific binding. A side-chain amino group and complementary hydrophobic domain were the principal features noted. The use of shape overlays, based on the pharmacophore, in conjunction with a chemical force field in the program ROCS, yielded discrimination between molecules classified as strong binders (experimental fraction unbound in microsomes<0.50) and those with a lower degree of binding (experimental fraction unbound in microsomes>0.50). In the initial data set of 56 molecules, 18 were classified as strong binders (on the basis of the above criteria), and all of those were recovered in the top 22 molecular hits from ROCS. Additionally, computationally generated values of log P were shown to provide a reasonable estimate of the fraction unbound in microsomes, providing the compounds were in their basic form at physiological pH.

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.

Application of data mining approaches to drug delivery

Computational approaches play a key role in all areas of the pharmaceutical industry from data mining, experimental and clinical data capture to pharmacoeconomics and adverse events monitoring. They will likely continue to be indispensable assets along with a growing library of software applications. This is primarily due to the increasingly massive amount of biology, chemistry and clinical data, which is now entering the public domain mainly as a result of NIH and commercially funded projects. We are therefore in need of new methods for mining this mountain of data in order to enable new hypothesis generation. The computational approaches include, but are not limited to, database compilation, quantitative structure activity relationships (QSAR), pharmacophores, network visualization models, decision trees, machine learning algorithms and multidimensional data visualization software that could be used to improve drug delivery after mining public and/or proprietary data. We will discuss some areas of unmet needs in the area of data mining for drug delivery that can be addressed with new software tools or databases of relevance to future pharmaceutical projects.

Structure-based methods for the prediction of drug metabolism

There is a tantalising possibility that we may be able to predict the metabolism of a drug directly from its structure, thus obviating the requirement for animal tests in this area. There are a number of techniques that can be used to estimate a range of events associated with metabolism, and may allow us to achieve this aim. This paper considers the role of (quantitative) structure-activity relationships, and pharmacophore and homology modelling in the prediction of metabolism. Examples are also presented where such approaches have been formalised into expert systems. Clearly, many advances have been made in this area in recent years. Discussed herein is the importance of fully integrating the diverse systems and approaches available to fulfil the aspiration to predict metabolism directly from structure.

In silico ADME modelling 2: Computational models to predict human serum albumin binding affinity using ant colony systems

Modelling of in vitro human serum albumin (HSA) binding data of 94 diverse drugs and drug-like compounds is performed to develop global predictive models that are applicable to the whole medicinal chemistry space. For this aim, ant colony systems, a stochastic method along with multiple linear regression (MLR), is employed to exhaustively search and select multivariate linear equations, from a pool of 327 molecular descriptors. This methodology helped us to derive optimal quantitative structure-property relationship (QSPR) models based on five and six descriptors with excellent predictive power. The best five-descriptor model is based on Kier and Hall valence connectivity index--Order 5 (path), Auto-correlation descriptor (Broto-Moreau) weighted by atomic masses--Order 4, Auto-correlation descriptor (Broto-Moreau) weighted by atomic polarizabilities--Order 5, AlogP98, SklogS (calculated buffer water solubility) [R=0.8942, Q=0.86790, F=62.24 and SE=0.2626]; the best six-variable model is based on Kier and Hall valence connectivity index of Order 3 (cluster), Auto-correlation descriptor (Broto-Moreau) weighted by atomic masses--Order 4, Auto-correlation descriptor (Broto-Moreau) weighted by atomic polarizabilities--Order 5, Atomic-Level-Based AI topological descriptors--AIdsCH, AlogP98, SklogS (calculated buffer water solubility) [R=0.9128, Q=0.89220, F=64.09 and SE=0.2411]. From the analysis of the physical meaning of the selected descriptors, it is inferred that the binding affinity of small organic compounds to human serum albumin is principally dependent on the following fundamental properties: (1) hydrophobic interactions, (2) solubility, (3) size and (4) shape. Finally, as the models reported herein are based on computed properties, they appear to be a valuable tool in virtual screening, where selection and prioritisation of candidates is required.

Focus on success: using a probabilistic approach to achieve an optimal balance of compound properties in drug discovery

The success of any drug will depend on how closely it achieves an ideal combination of potency, selectivity, pharmacokinetics and safety. The key to achieving this success efficiently is to consider the overall balance of molecular properties of compounds against the ideal profile for the therapeutic indication from the earliest stages of a drug discovery project. The use of in silico predictive models of absorption, distribution, metabolism and elimination (ADME) and physicochemical properties is a major aid in this exercise, as it enables virtual molecules to be assessed across a broad range of properties from initial library generation, through to candidate selection. Of course, no measurement, whether in silico, in vitro or in vivo, is perfect and the uncertainties in any data should be explicitly taken into account when basing conclusions on test results. In addition, in the early stages of drug discovery, when designing a library that is lead seeking or building compound structure-activity relationships, the quality of any set of molecules should also be balanced against the chemical diversity covered. Here, a scheme is presented for achieving these goals based on a suite of predictive ADME models, probabilistic scoring and multiobjective optimisation for library design. The use of this platform for applications in lead identification and optimisation is illustrated.

Reengineering the pharmaceutical industry by crash-testing molecules

The recent decline in drug approvals and the increase in late-stage failures indicate that the ability to generate and screen large numbers of molecules has not improved the drug pipeline. Perhaps the pharmaceutical industry should follow the example of the automotive industry and agree upon a shared modeling language with vendors and academics to enable integration of predictive computational tools across the industry. This will then enable the virtual 'crash-testing' of drugs before synthesis, biological testing and, most importantly, clinical trials. This represents an ambitiously progressive approach using the models for simulating every stage of the drug discovery and development process. Combining the relevant computational algorithms into a grand unified model would enable prioritization of the best ideas before pursuing a discovery program, selecting a target or synthesizing a molecule. The successful application of these virtual crash-testing principles by any of its current proponents could revitalize the pharmaceutical industry so that failure is avoided.

Computational prediction of human drug metabolism

There is an urgent requirement within the pharmaceutical and biotechnology industries, regulatory authorities and academia to improve the success of molecules that are selected for clinical trials. Although absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties are some of the many components that contribute to successful drug discovery and development, they represent factors for which we currently have in vitro and in vivo data that can be modelled computationally. Understanding the possible toxicity and the metabolic fate of xenobiotics in the human body is particularly important in early drug discovery. There is, therefore, a need for computational methodologies for uncovering the relationships between the structure and the biological activity of novel molecules. The convergence of numerous technologies, including high-throughput techniques, databases, ADME/Tox modelling and systems biology modelling, is leading to the foundation of systems-ADME/Tox. Results from experiments can be integrated with predictions to globally simulate and understand the likely complete effects of a molecule in humans. The development and early application of major components of MetaDrug (GeneGo, Inc.) software will be described, which includes rule-based metabolite prediction, quantitative structure-activity relationship models for major drug metabolising enzymes, and an extensive database of human protein-xenobiotic interactions. This represents a combined approach to predicting drug metabolism. MetaDrug can be readily used for visualising Phase I and II metabolic pathways, as well as interpreting high-throughput data derived from microarrays as networks of interacting objects. This will ultimately aid in hypothesis generation and the early triaging of molecules likely to have undesirable predicted properties or measured effects on key proteins and cellular functions.

Systems-ADME/Tox: resources and network approaches

The increasing cost of drug development is partially due to our failure to identify undesirable compounds at an early enough stage of development. The application of higher throughput screening methods have resulted in the generation of very large datasets from cells in vitro or from in vivo experiments following the treatment with drugs or known toxins. In recent years the development of systems biology, databases and pathway software has enabled the analysis of the high-throughput data in the context of the whole cell. One of the latest technology paradigms to be applied alongside the existing in vitro and computational models for absorption, distribution, metabolism, excretion and toxicology (ADME/Tox) involves the integration of complex multidimensional datasets, termed toxicogenomics. The goal is to provide a more complete understanding of the effects a molecule might have on the entire biological system. However, due to the sheer complexity of this data it may be necessary to apply one or more different types of computational approaches that have as yet not been fully utilized in this field. The present review describes the data generated currently and introduces computational approaches as a component of ADME/Tox. These methods include network algorithms and manually curated databases of interactions that have been separately classified under systems biology methods. The integration of these disparate tools will result in systems-ADME/Tox and it is important to understand exactly what data resources and technologies are available and applicable. Examples of networks derived with important drug transporters and drug metabolizing enzymes are provided to demonstrate the network technologies.

Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates

MOTIVATION

To gather information about available databases and chemoinformatics methods for prediction of properties relevant to the drug discovery and optimization process.

RESULTS

We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability of chemical compounds as potential drugs, as well as for predicting their physico-chemical and ADMET properties have been proposed in recent years. These methods are discussed, and some possible future directions in this rapidly developing field are described.

QUANTITATIVE STRUCTURE-METABOLISM RELATIONSHIP MODELING OF METABOLICN-DEALKYLATION REACTION RATES

It is widely recognized that preclinical drug discovery can be improved via the parallel assessment of bioactivity, absorption, distribution, metabolism, excretion, and toxicity properties of molecules. High-throughput computational methods may enable such assessment at the earliest, least expensive discovery stages, such as during screening compound libraries and the hit-to-lead process. As an attempt to predict drug metabolism and toxicity, we have developed an approach for evaluation of the rate of N-dealkylation mediated by two of the most important human cytochrome P450s (P450), namely CYP3A4 and CYP2D6. We have taken a novel approach by using descriptors generated for the whole molecule, the reaction centroid, and the leaving group, and then applying neural network computations and sensitivity analysis to generate quantitative structure-metabolism relationship models. The quality of these models was assessed by using the cross-validated correlation coefficients of 0.82 for CYP3A4 and 0.79 for CYP2D6 as well as external test molecules for each enzyme. The relative performance of different neural networks was also compared, and modular neural networks with two hidden layers provided the best predictive ability. Functional dependencies between the neural network input and output variables, generalization ability, and limitations of the described approach are also discussed. These models represent an initial approach to predicting the rate of P450-mediated metabolism and may be applied and integrated with other models for P450 binding to produce a systems-based approach for predicting drug metabolism.

KOHONEN MAPS FOR PREDICTION OF BINDING TO HUMAN CYTOCHROME P450 3A4

The drug development process utilizes the parallel assessment of activity at a therapeutic target as well as absorption, distribution, metabolism, excretion, and toxicity properties of molecules. The development of novel, reliable, and inexpensive computational methods for the early assessment of metabolism and toxicity is becoming increasingly an important part of this process. We have used a computational approach for the assessment of drugs and drug-like compounds which bind to the cytochromes P450 (P450s) with experimentally determined Km values. The physicochemical properties of these compounds were calculated using molecular descriptor software and then analyzed using Kohonen self-organizing maps. This approach was applied to generate a P450-specific classification of nearly 500 drug compounds. We observed statistically significant differences in the molecular properties of low Km molecules for various P450s and suggest a relationship between 33 of these compounds and their CYP3A4-inhibitory activity. A test set of additional CYP3A4 inhibitors was used, and 13 of 15 of these molecules were colocated in the regions of low Km values. This computational approach represents a novel method for use in the generation of metabolism models, enabling the scoring of libraries of compounds for their Km values to numerous P450s.

BREED: Generating Novel Inhibitors through Hybridization of Known Ligands. Application to CDK2, P38, and HIV Protease

In this work we describe BREED, a method for the generation of novel inhibitors from structures of known ligands bound to a common target. The method is essentially an automation of the common medicinal chemistry practice of joining fragments of two known ligands to generate a new inhibitor. The ligand-bound target structures are overlaid, all overlapping bonds in all pairs of ligands are found, and the fragments on each side of each matching bond are swapped to generate the new molecules. Since the method is automated, it can be applied recursively to generate all possible combinations of known ligands. In an application of this method to HIV protease inhibitors and protein kinase inhibitors, hundreds of new molecular structures were generated. These included known inhibitor scaffolds not included in the initial set, entirely novel scaffolds, and novel substituents on known scaffolds. The method is fast, and since all of the ligand functional groups are known to bind the target in the precise position and orientation present in the novel ligand, the success rate of this method should be superior to more traditional de novo design techniques. In an era of increasingly high-throughput structural biology, such methods for high-throughput utilization of structural information will become increasingly valuable.