High-throughput screening: update on practices and success

High-throughput screening (HTS) has become an important part of drug discovery at most pharmaceutical and many biotechnology companies worldwide, and use of HTS technologies is expanding into new areas. Target validation, assay development, secondary screening, ADME/Tox, and lead optimization are among the areas in which there is an increasing use of HTS technologies. It is becoming fully integrated within drug discovery, both upstream and downstream, which includes increasing use of cell-based assays and high-content screening (HCS) technologies to achieve more physiologically relevant results and to find higher quality leads. In addition, HTS laboratories are continually evaluating new technologies as they struggle to increase their success rate for finding drug candidates. The material in this article is based on a 900-page HTS industry report involving 54 HTS directors representing 58 HTS laboratories and 34 suppliers.     

In vitro ADME profiling using high-throughput rapidfire mass spectrometry: cytochrome p450 inhibition and metabolic stability assays

Early assessment of absorption, distribution, metabolism, and excretion (ADME) properties of drug candidates has become an essential component of modern drug discovery. ADME characterization is important in identifying compounds early that are likely to fail in later clinical development because of suboptimal pharmacokinetic properties or undesirable drug-drug interactions. Proper utilization of ADME results, meanwhile, can prioritize candidates that are more likely to have good pharmacokinetic properties and also minimize potential drug-drug interactions. By integrating a RapidFire system with an API4000 mass spectrometer (RF-MS), we have established a high-throughput capability to profile compounds (>100 compounds/wk) in a panel of ADME assays in parallel with biochemical and cellular characterizations. Cytochrome P450 inhibition and time-dependent inhibition assays and microsomal stability assays were developed and fully optimized on the system. Compared with the classic liquid chromatography-mass spectrometry method, the RF-MS system generates consistent data with approximately 20-fold increase in throughput. The lack of chromatographic separation of compounds, substrates, and metabolites can complicate data interpretation, but this occurs in a small number of cases that are readily identifiable. Overall, this system has enabled a real-time and quantitative measurement of a large number of ADME samples, providing a rapid evaluation of clinically important drug-drug interaction potential and drug metabolic stability.     

High-throughput screening of biocatalytic activity: applications in drug discovery

Enzymes catalyze a diverse set of reactions that propel life's processes and hence serve as valuable therapeutic targets. High-throughput screening methods have become essential for sifting through large chemical libraries in search of drug candidates, and several sensitive and reliable analytical techniques have been specifically adapted to high-throughput measurements of biocatalytic activity. High-throughput biocatalytic assay platforms thus enable rapid screening against enzymatic targets, and have vast potential to impact various stages of the drug discovery process, including lead identification and optimization, and ADME/Tox assessment. These advances are paving the way for the adoption of high-throughput biocatalytic assays as an indispensable tool for the pharmaceutical industry.     

Development and application of physiologically based pharmacokinetic-modeling tools to support drug discovery

Physiologically based pharmacokinetic (PBPK) modeling integrates physicochemical (PC) and in vitro pharmacokinetic (PK) data using a mechanistic framework of principal ADME (absorption, distribution, metabolism, and excretion) processes into a physiologically based whole-body model. Absorption, distribution, and clearance are modeled by combining compound-specific PC and PK properties with physiological processes. Thereby, isolated in vitro data can be upgraded by means of predicting full concentration-time profiles prior to animal experiments. The integrative process of PBPK modeling leads to a better understanding of the specific ADME processes driving the PK behavior in vivo, and has the power to rationally select experiments for a more focussed PK project support. This article presents a generic disposition model based on tissue-composition-based distribution and directly scaled hepatic clearance. This model can be used in drug discovery to identify the critical PK issues of compound classes and to rationally guide the optimization path of the compounds toward a viable development candidate. Starting with a generic PBPK model, which is empirically based on the most common PK processes, the model will be gradually tailored to the specifics of drug candidates as more and more experimental data become available. This will lead to a growing understanding of the 'drug in the making', allowing a range of predictions to be made for various purposes and conditions. The stage is set for a wide penetration of PK modeling and simulations to form an intrinsic part of a project starting from lead discovery, to lead optimization and candidate selection, to preclinical profiling and clinical trials.     

In vitro preclinical lead optimisation technologies (PLOTs) in pharmaceutical development

The explosion of genuine high throughput technologies has allowed large compound libraries to be screened with ever-increasing biological specificity, exacerbating the problem of lead candidate selection for subsequent drug development. To avoid creating a bottleneck, compounds identified from the high throughput screens undergo lead optimisation by employing medium-throughput screen which permits ranking in terms of their basic absorption, distribution, metabolism, excretion (ADME) and toxicological properties. The historical role of the CRO in the drug discovery/development continuum has been to perform efficacy and toxicology studies, simply to support the regulatory submission of lead candidates. This situation is, however, changing with the development of preclinical lead optimisation technologies facilitating the selection of leading candidates, thereby bridging the gap between high throughput efficacy screens and conventional safety assessment programmes.     

Recent advances in applications of liquid chromatography-tandem mass spectrometry to the analysis of reactive drug metabolites

Biotransformation of chemically stable compounds to reactive metabolites which can bind covalently to macromolecules, such as proteins and DNA, is considered as an undesirable feature of drug candidates. As part of an overall assessment of absorption, distribution, metabolism and excretion (ADME) properties, many pharmaceutical companies have put methods in place to screen drug candidates for their tendency to generate reactive metabolites and as well characterize the nature of the reactive metabolites through in vitro and in vivo studies. After identification of the problematic compounds, steps can be taken to minimize the potential of bioactivation through appropriate structural modifications. For these reasons, detection, structural characterization and quantification of reactive metabolites by mass spectrometry have become an important task in the drug discovery process. Triple quadrupole mass spectrometry is traditionally employed for the analysis of reactive metabolites. In the past 3 years, a number of new mass spectrometry methodologies have been developed to improve the sensitivity, selectivity and throughput of the analysis. This review focuses on the recent advances in the detection and characterization of reactive metabolites by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in drug discovery and development, especially through the use of linear ion trap (LTQ), hybrid triple quadrupole-linear ion trap (Q-trap) and the high resolution LTQ-Orbitrap instruments.     

秀丽隐杆线虫（Caenorhabditis elegans）在药物筛选中的应用

基于靶点的体外药物筛选操作相对简单,成本较低,但是由于药物在体内的作用并不仅仅取决于其与靶点的作用程度,吸收、分布、代谢、排泄特征和毒性均会对早期先导物能否进入临床使用产生极大的影响,因此,药物的体内筛选受到重视。本文重点综述了秀丽隐杆线虫（C.elegans）在抗衰老、抗感染药物筛选中的应用情况。秀丽隐杆线虫结构简单、易于培养和可实现高通量筛选,在未来的药物筛选中必将发挥更重要的作用。     

High-throughput human metabolism and toxicity analysis

Poor drug candidate safety profiles are often identified late in the drug development process, manifesting themselves in the preclinical and clinical phases and significantly contributing to the high cost and low yield of drug discovery. As a result, new tools are needed to accelerate the assessment of drug candidate toxicity and human metabolism earlier in the drug development process, from primary drug candidate screening to lead optimization. Although high-throughput screens exist for much of the discovery phase of drug development, translating such screening techniques into platforms that can accurately mimic the human in vivo response and predict the impact of drug candidates on human toxicology has proven difficult. Nevertheless, some success has been achieved in recent years, which may ultimately yield widespread acceptance in the pharmaceutical industry.     

Drug discovery in academia

Drug discovery and development is generally done in the commercial rather than the academic realm. Drug discovery involves target discovery and validation, lead identification by high-throughput screening, and lead optimization by medicinal chemistry. Follow-up preclinical evaluation includes analysis in animal models of compound efficacy and pharmacology (ADME: administration, distribution, metabolism, elimination) and studies of toxicology, specificity, and drug interactions. Notwithstanding the high-cost, labor-intensive, and non-hypothesis-driven aspects of drug discovery, the academic setting has a unique and expanding niche in this important area of investigation. For example, academic drug discovery can focus on targets of limited commercial value, such as third-world and rare diseases, and on the development of research reagents such as high-affinity inhibitors for pharmacological "gene knockout" in animal models ("chemical genetics"). This review describes the practical aspects of the preclinical drug discovery process for academic investigators. The discovery of small molecule inhibitors and activators of the cystic fibrosis transmembrane conductance regulator is presented as an example of an academic drug discovery program that has yielded new compounds for physiology research and clinical development. high-throughput screening; drug development; pharmacology; fluorescence; cystic fibrosis transmembrane conductance regulator     

Hologram QSAR model for the prediction of human oral bioavailability

A drug intended for use in humans should have an ideal balance of pharmacokinetics and safety, as well as potency and selectivity. Unfavorable pharmacokinetics can negatively affect the clinical development of many otherwise promising drug candidates. A variety of in silico ADME (absorption, distribution, metabolism, and excretion) models are receiving increased attention due to a better appreciation that pharmacokinetic properties should be considered in early phases of the drug discovery process. Human oral bioavailability is an important pharmacokinetic property, which is directly related to the amount of drug available in the systemic circulation to exert pharmacological and therapeutic effects. In the present work, hologram quantitative structure–activity relationships (HQSAR) were performed on a training set of 250 structurally diverse molecules with known human oral bioavailability. The most significant HQSAR model (q² = 0.70, r² = 0.93) was obtained using atoms, bond, connection, and chirality as fragment distinction. The predictive ability of the model was evaluated by an external test set containing 52 molecules not included in the training set, and the predicted values were in good agreement with the experimental values. The HQSAR model should be useful for the design of new drug candidates having increased bioavailability as well as in the process of chemical library design, virtual screening, and high-throughput screening.     

ECVAM and pharmaceuticals

In the pharmaceutical industry, toxicology testing is normally done by preclinical scientists during the Development phase. In the last decade, the implementation of high-throughput screens during the Discovery phase has resulted in an ever-increasing number of lead candidates to be selected for drug development. The low throughput of the conventional safety tests is a bottleneck in the drug-development process. The pharmaceutical industry needs new techniques, down-scaled tests and in vitro alternative test models to determine the absorption, distribution, metabolism, and excretion (ADME) and toxicology profiles of compounds in the late-Discovery phase and/or early in the Development phase. Medium-throughput ADME and toxicity tests will enhance the selection of safer new chemical entities for animals and/or humans. Consequently, this testing strategy will not only reduce the use of resources and the overall development time, but will also result in a substantial decrease in animal use.     

QSAR and ADME

The prediction from structure of ADME (absorption, distribution, metabolism, elimination) of drug candidates is an important goal to achieve since it can considerably reduce the cost of drug development. Using our database of 10,700 QSAR, we are now reaching the point where we can make many useful comparisons that illustrate how ADME is a practical way to describe the way organic compounds react with living systems. We also show that Caco-2 cells are useful to model absorption, but the most generally useful parameter is the octanol/water partition coefficient. It should be noted, however, that in our opinion, an in silico prediction of ADME is still a long way in the future.     

Artificial intelligence in drug design

Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology, the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials. Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence (AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening, activity scoring, quantitative structure-activity relationship (QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity (ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability, deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules, which will further promote the application of AI technologies in the field of drug design.     

Key factors influencing ADME properties of therapeutic proteins: A need for ADME characterization in drug discovery and development

Protein therapeutics represent a diverse array of biologics including antibodies, fusion proteins, and therapeutic replacement enzymes. Since their inception, they have revolutionized the treatment of a wide range of diseases including respiratory, vascular, autoimmune, inflammatory, infectious, and neurodegenerative diseases, as well as cancer. While in vivo pharmacokinetic, pharmacodynamic, and efficacy studies are routinely carried out for protein therapeutics, studies that identify key factors governing their absorption, distribution, metabolism, and excretion (ADME) properties have not been fully investigated. Thorough characterization and in-depth study of their ADME properties are critical in order to support drug discovery and development processes for the production of safer and more effective biotherapeutics. In this review, we discuss the main factors affecting the ADME characteristics of these large macromolecular therapies. We also give an overview of the current tools, technologies, and approaches available to investigate key factors that influence the ADME of recombinant biotherapeutic drugs, and demonstrate how ADME studies will facilitate their future development.     

Integrating computer-based de novo drug design and multidimensional filtering for desirable drugs

In the pharmaceutical industry today, many of the potent compounds discovered using expensive technologies are eventually rejected because of poor physicochemical or absorption, distribution, metabolism, excretion and toxicology (ADME/Tox) properties. This problem can be addressed by placing fast and accurate computational technologies at the heart of drug discovery. Chemically diverse and potent compounds generated by de novo design algorithms are scored for ADME/Tox properties using rigorously validated statistical models. Every molecule passing through this in silico pipeline is thus associated with a wealth of predicted properties, thereby allowing for rapid assessment to determine which molecule should be further developed. Critical to this idea is a platform that allows for the efficient exchange of in silico and experimental data between all scientists regardless of specialization. By bridging the gap between the in silico and experimental cultures in this fashion, an information-driven, cost-effective drug discovery program can be realized.     

PK/DB: database for pharmacokinetic properties and predictive in silico ADME models

The study of pharmacokinetic properties (PK) is of great importance in drug discovery and development. In the present work, PK/DB (a new freely available database for PK) was designed with the aim of creating robust databases for pharmacokinetic studies and in silico absorption, distribution, metabolism and excretion (ADME) prediction. Comprehensive, web-based and easy to access, PK/DB manages 1203 compounds which represent 2973 pharmacokinetic measurements, including five models for in silico ADME prediction (human intestinal absorption, human oral bioavailability, plasma protein binding, blood-brain barrier and water solubility). AVAILABILITY: http://www.pkdb.ifsc.usp.br     

Plant natural products: Back to the future or into extinction?

Natural product substances have historically served as the most significant source of new leads for pharmaceutical development. However, with the advent of robotics, bioinformatics, high throughput screening (HTS), molecular biology-biotechnology, combinatorial chemistry, in silico (molecular modeling) and other methodologies, the pharmaceutical industry has largely moved away from plant derived natural products as a source for leads and prospective drug candidates. Can, or will, natural products ever recapture the preeminent position they once held as a foundation for drug discovery and development? The challenges associated with development of natural products as pharmaceuticals are illustrated by the Taxol^® story. Several misconceptions, which constrain utilization of plant natural products, for discovery and development of pharmaceuticals, are addressed to return natural products to the forefront.     

Industrializing AI/ML during the end-to-end drug discovery process

Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all stages of drug discovery from target identification to drug candidate selection. Recently, several artificial intelligence and machine learning (AI/ML)-based drug discovery companies have attempted to build data-driven platforms spanning the end-to-end drug discovery process. The ability to identify elusive targets essentially leads to the diversification of discovery pipelines, thereby increasing the ability to address unmet needs. Modern ML technologies are complementing traditional computer-aided drug discovery by accelerating candidate optimization in innovative ways. This review summarizes recent developments in AI/ML methods from target identification to molecule optimization, and concludes with an overview of current industrial trends in end-to-end AI/ML platforms.     

<Emphasis Type="Italic">ADDME – Avoiding Drug Development Mistakes Early</Emphasis>: central nervous system drug discovery perspective

The advent of early absorption, distribution, metabolism, excretion, and toxicity (ADMET) screening has increased the attrition rate of weak drug candidates early in the drug-discovery process, and decreased the proportion of compounds failing in clinical trials for ADMET reasons. This paper reviews the history of ADMET screening and its place in pharmaceutical development, and central nervous system drug discovery in particular. Assays that have been developed in response to specific needs and improvements in technology that result in higher throughput and greater accuracy of prediction of human mechanisms of absorption and toxicity are discussed. The paper concludes with the authors' forecast of new models that will better predict human efficacy and toxicity.     

Lead compounds discovered from libraries: part 2

Many lead compounds with the potential to progress to viable drug candidates have been identified from libraries during the past two years. There are two key strategies most often employed to find leads from libraries: first, high-throughput biological screening of corporate compound collections; and second, synthesis and screening of project-directed libraries (i.e. target-based libraries). Numerous success stories, including the discovery of several clinical candidates, testify to the utility of chemical library collections as proven sources of new leads for drug development.