Systems biology in drug safety and metabolism: integration of microarray, real-time PCR and enzyme approaches

The last decade has seen a rapid expansion in the field of functional genomics, due mainly to the global gene expression profiling capabilities provided by techniques, such as microarray analysis. Application of these technologies in fields as diverse as plant research, to public health and environmental sciences, forensic science and drug research, shows the versatility of these tools and the promise they hold for revolutionizing research in the life sciences. In drug discovery, attempts have been made to use functional genomics in target identification and validation, lead selection and optimization, and in preclinical studies to predict clinical outcome. These studies have provided a plethora of data and undoubtedly expanded our understanding of genetic alterations in diseased and non-diseased states, but the benefits that these technologies hold have not yet been fully realized. This review discusses how a comprehensive approach to gene regulation studies, a 'systems biology' approach, is being applied in a drug development setting to address mechanism-based questions and issues raised by regulatory authorities.     

The role of mass spectrometry in biomarker discovery and measurement

Recent advances in the biological and analytical sciences have led to unprecedented interest in the discovery and quantitation of endogenous molecules that serve as indicators of drug safety, mechanism of action, efficacy, and disease state progression. By allowing for improved decision-making, these indicators, referred to as biomarkers, can dramatically improve the efficiency of drug discovery and development. Mass spectrometry has been a key part of biomarker discovery and evaluation owing to several important attributes, which include sensitive and selective detection, multi-analyte analysis, and the ability to provide structural information. Because of these capabilities, mass spectrometry has been widely deployed in search for new markers both through the analysis of large molecules (proteomics) and small molecules (metabonomics). In addition, mass spectrometry is increasingly being used to support quantitative measurement to assist in the evaluation and validation of biomarker leads. In this review, the dual role of mass spectrometry for biomarker discovery and measurement is explored for both large and small molecules by examining the key technologies and methods used along the continuum from drug discovery through clinical development.     

The state of the art of the science of drug discovery—an opinion

The discovery of drugs (and their subsequent development) is a difficult subspeciality in pharmacology that, in conjunction with clinical medicine, generates the vitality underlying the entire pharmacologic-therapeutic enterprise. The drug discovery process is unique among biomedical sciences in that it takes place almost solely in industrial research institutions rather than in nonindustrial institutions—i.e., academic and government laboratories. The conventional view of drug discovery is that, since it is mission-oriented and pharmacological, it is derivative on the basic pharmacological research carried out in nonindustrial research institutions. It is argued by the author that this model is oversimplistic and is not applicable to pharmaceutical research: It is very frequently the case that drugs are developed first and thus precede “basic” knowledge and in fact are the source of new academic findings, especially in pharmacology. In addition, new drugs are useful tools in physiology and biochemistry. Thus, the discovery of an innovative drug is per se a basic contribution. A more reasonable model of the relationship between drug discovery (and development) and nonindustrial research—physiological, biochemical, and pharmacological—is that of symbiosis with each segment nurturing the other to mutual benefit. The problems of pharmacologists involved in drug development are more complex than those of nonindustrial pharmacologists in that the end points of their work—successful clinical trial and marketing—demand more than an interesting pharmacological action; the action must be of therapeutic significance and be resident in a molecule which has been determined to be nontoxic, bioavailable, patentable, and commercially viable. The differences in goals between academic (non-mission-oriented) and industrial (mission-oriented) pharmacological research and the consequent demands in the latter for specified kinds of results often leads to differing patterns of research and research judgments that can be a source of misunderstandings and confusion between industrial and nonindustrial pharmacologists.     

Translational paradigms in pharmacology and drug discovery

The translational sciences represent the core element in enabling and utilizing the output from the biomedical sciences and to improving drug discovery metrics by reducing the attrition rate as compounds move from preclinical research to clinical proof of concept. Key to understanding the basis of disease causality and to developing therapeutics is an ability to accurately diagnose the disease and to identify and develop safe and effective therapeutics for its treatment. The former requires validated biomarkers and the latter, qualified targets. Progress has been hampered by semantic issues, specifically those that define the end product, and by scientific issues that include data reliability, an overt reductionistic cultural focus and a lack of hierarchically integrated data gathering and systematic analysis. A necessary framework for these activities is represented by the discipline of pharmacology, efforts and training in which require recognition and revitalization.     

Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases

The search for effective drugs to treat new and existing diseases is a laborious one requiring a large investment of capital, resources, and time. The coronavirus 2019 (COVID-19) pandemic has been a painful reminder of the lack of development of new antimicrobial agents to treat emerging infectious diseases. Artificial intelligence (AI) and other in silico techniques can drive a more efficient, cost-friendly approach to drug discovery by helping move potential candidates with better clinical tolerance forward in the pipeline. Several research teams have developed successful AI platforms for hit identification, lead generation, and lead optimization. In this review, we investigate the technologies at the forefront of spearheading an AI revolution in drug discovery and pharmaceutical sciences.     

Drug Repurposing from an Academic Perspective

Academia and small business research units are poised to play an increasing role in drug discovery, with drug repurposing as one of the major areas of activity. Here we summarize project status for a number of drugs or classes of drugs: raltegravir, cyclobenzaprine, benzbromarone, mometasone furoate, astemizole, R-naproxen, ketorolac, tolfenamic acid, phenothiazines, methylergonovine maleate and beta-adrenergic receptor drugs, respectively. Based on this multi-year, multi-project experience we discuss strengths and weaknesses of academic-based drug repurposing research. Translational, target and disease foci are strategic advantages fostered by close proximity and frequent interactions between basic and clinical scientists, which often result in discovering new modes of action for approved drugs. On the other hand, lack of integration with pharmaceutical sciences and toxicology, lack of appropriate intellectual coverage and issues related to dosing and safety may lead to significant drawbacks. The development of a more streamlined regulatory process world-wide, and the development of pre-competitive knowledge transfer systems such as a global healthcare database focused on regulatory and scientific information for drugs world-wide, are among the ideas proposed to improve the process of academic drug discovery and repurposing, and to overcome the "valley of death" by bridging basic to clinical sciences.     

Use of ex vivo systems for biomarker discovery

Patients exhibit a range of responses to drug treatment owing to individual genetic variation and biology. A deeper understanding of the human genome, enabled by increasingly powerful technologies to measure both its genes and gene products, has unleashed the concept of tailoring therapy to the individual patient upon pharmaceutical and clinical sciences. The successful application of personalized medicine depends upon the discovery and development of biomarkers. Biomarkers that either indicate pharmacodynamic effects or constitute predictive measures of individual patient responses can support dose selection and/or help determine therapeutic options. The development of biomarkers for clinical testing and validation can be facilitated by the use of ex vivo systems utilizing clinically relevant human tissues for the discovery of biomarkers of drug activity before first in human (FIH) studies. In this review we discuss the uses of ex vivo systems for both disease tissues and surrogate normal tissues to provide mechanistic insights into drug action and for the purpose of identifying candidate biomarkers.     

Fifty years of Biochemical Pharmacology: the discipline and the journal

The discipline of biochemical pharmacology emerged in the late 1940s as a result of an increasing emphasis on understanding drug mechanisms at the cellular level. This research approach has contributed significantly to the development of many new drug classes including antihypertensive, antifective, cholesterol lowering, anti-inflammatory, and anticancer agents, as well as antipsychotics, antidepressants and anxiolytics. Biochemical pharmacology remains a major tool in drug discovery, being employed in the search for novel therapeutics for the above and other conditions and clinical challenges, such as neurodegenerative disorders, for the treatment of pain, and for development of agents that do not induce, or can overcome, antibiotic/antiviral resistance. Together with chemical, molecular, genetic, physiological, and clinical sciences, biochemical pharmacology will in the coming decades continue to be a critical component of the drug discovery process.     

Serum and urine bone resorption markers and pharmacokinetics of the cathepsin K inhibitor ONO-5334 after ascending single doses in post menopausal women

Historically, much drug discovery and development in psychopharmacology tended to be empirical. However, over the last 20 years it has primarily been target oriented, with synthesis and selection of compounds designed to act at a specific neurochemical site. Such compounds are then examined in functional animal models of disease. There is little evidence that this approach (which we call 'targetophilia') has enhanced the discovery process and some indications that it may have retarded it. A major problem is the weakness of many animal models in mimicking the disease and the lack of appropriate biochemical markers of drug action in animals and patients. In this review we argue that preclinical studies should be conducted as if they were clinical studies in design, analysis, and reporting, and that clinical pharmacologists should be involved at the earliest stages, to help ensure that animal models reflect as closely as possible the clinical disease. In addition, their familiarity with pharmacokinetic-pharmacodynamic integration (PK-PD) would help ensure that appropriate dosing and drug measurement techniques are applied to the discovery process, thereby producing results with relevance to therapeutics. Better integration of experimental and clinical pharmacologists early in the discovery process would allow observations in animals and patients to be quickly exchanged between the two disciplines. This non-linear approach to discovery used to be the way research proceeded, and it resulted in productivity that has never been bettered. It also follows that occasionally 'look-see' studies, a proven technique for drug discovery, deserve to be reintroduced.     

High-throughput electronic biology: mining information for drug discovery

The vast range of in silico resources that are available in life sciences research hold much promise towards aiding the drug discovery process. To fully realize this opportunity, computational scientists must consider the practical issues of data integration and identify how best to apply these resources scientifically. In this article we describe in silico approaches that are driven towards the identification of testable laboratory hypotheses; we also address common challenges in the field. We focus on flexible, high-throughput techniques, which may be initiated independently of 'wet-lab' experimentation, and which may be applied to multiple disease areas. The utility of these approaches in drug discovery highlights the contribution that in silico techniques can make and emphasizes the need for collaboration between the areas of disease research and computational science.     

The Pharmaceutical Sciences in 2020: Report of a Conference Organized by the Board of Pharmaceutical Sciences of the International Pharmaceutical Federation (FIP)

The Board of Pharmaceutical Sciences (BPS) of the International Pharmaceutical Federation (FIP) has developed a view on the future of pharmaceutical sciences in 2020. This followed an international conference with invited participants from various fields (academicians, scientists, regulators, industrialists, venture capitalists) who shared their views on the forces that might determine how the pharmaceutical sciences will look in 2020. The commentary here provides a summary of major research activities that will drive drug discovery and development, enabling technologies for pharmaceutical sciences, paradigm shifts in drug discovery, development and regulations, and changes in education to meet the demands of academia, industry and regulatory institutions for pharmaceutical sciences in 2020.     

生物标志物在药品生命周期中的应用与展望

生物标志物(biomarker)是一种能客观测量并评价正常生物过程、病理过程或对药物干预反应的指示物,可有效提高新药研究开发决策,指导候选药物早期临床试验,降低新药研发失败的风险,其在药品生命周期中的重要作用已引起业内普遍关注.欧美等国家和地区相继出台关于生物标志物研究开发和资格鉴定程序的指南,鼓励医药企业将生物标志物...     

Quantitative Systems Pharmacology for Rare Disease Drug Development

Though hundreds of drugs have been approved by the US Food and Drug Administration (FDA) for treating various rare diseases, most rare diseases still lack FDA-approved therapeutics. To identify the opportunities for developing therapies for these diseases, the challenges of demonstrating the efficacy and safety of a drug for treating a rare disease are highlighted herein. Quantitative systems pharmacology (QSP) has increasingly been used to inform drug development; our analysis of QSP submissions received by FDA showed that there were 121 submissions as of 2022, for informing rare disease drug development across development phases and therapeutic areas. Examples of published models for inborn errors of metabolism, non-malignant hematological disorders, and hematological malignancies were briefly reviewed to shed light on use of QSP in drug discovery and development for rare diseases. Advances in biomedical research and computational technologies can potentially enable QSP simulation of the natural history of a rare disease in the context of its clinical presentation and genetic heterogeneity. With this function, QSP may be used to conduct in-silico trials to overcome some of the challenges in rare disease drug development. QSP may play an increasingly important role in facilitating development of safe and effective drugs for treating rare diseases with unmet medical needs.     

Alternative strategies in drug development: clinical pharmacological aspects

Due to the continuous increase in time and cost of drug development and the considerable amount of resources required by the traditional approach, companies can no longer afford to continue to late phase 3 with drugs which are unlikely to be therapeutically effective. The future challenge must be for the pharmaceutical industry to slash its research and development costs by achieving a significant cut in the attrition rate for drugs entering preclinical and clinical development, and to reduce the development time and to increase the probability of success in later clinical trials by streamlining the development processes. In the 100 years to 1995, the pharmaceutical industry worked on about 500 targets with a limited number of compounds, whereas now, using new technologies like genomics, high throughput screening and combinatorial chemistry, drug companies will see an explosion in the number of targets and leads it can explore. Therefore, a tough selection process for picking candidate compounds out of research and a quick kill process for the candidate, which does not measure up in advanced trials, is mandatory to avoid wasting time, energy and money. To improve the transition from research to development it is necessary to validate new targets, define success criteria for research, integrate bioinformation at every stage in drug discovery, define prerequisites for development, identify the "losers" and select the "winners" early and concentrate efforts on them, and to automate the research and development (R&D) process to optimize resource requirements versus time lines and to ensure effective flow of information from drug discovery to late phase of development. In drug development a deeper understanding of a drugs' action is necessary from animal models and phase I, IIa studies prior to taking the drug further in development. Instead of moving from discovery thorough development phases in sequential steps, drug development should be streamlined combining preclinical and early clinical development as an exploratory stage and phases IIb, III as a confirmatory stage. Preclinical and clinical-pharmacological studies in the exploratory stage of drug development should be designed for decision making in contrast to later clinical trials that require power for proof-of-safety and efficacy. Strategies to improve the quality of decisions in drug development are: the use and integration of new tools and technologies such as pharmacogenomics to improve our knowledge about the origin of the disease and to identify new therapeutic strategies; modelling and simulation of preclinical and clinical trials to bridge the gap between the early stages of the development of a new drug and its potential effects in humans; more sophisticated clinical pharmacokinetics to answer the question if the drug is present at the disease site for a sufficient time and to provide information on concentration-effect-relationships; selecting and evaluating surrogates/biomarkers for safety and efficacy; involvement of the target population as soon as possible; using information technologies to make better use of existing data. The more thorough and profound studies have been carried out during this exploratory stage of development, the earlier a decision can be made on the continuation or discontinuation of further development, thus saving development time and money and assessing and considerably reducing the risk for the patients and increasing the success-rate of the project in the later confirmatory effectiveness trial. Taking responsibility as the link between research and development gives clinical pharmacology a major opportunity to assume a pivotal role in research and development of new drugs. To reach this goal, clinical pharmacology must be fully integrated in the whole process from the candidate selection to its approval.     

The Therapeutic Target Database: an internet resource for the primary targets of approved, clinical trial and experimental drugs

Increasing numbers of proteins, nucleic acids and other molecular entities have been explored as therapeutic targets. A challenge in drug discovery is to decide which targets to pursue from an increasing pool of potential targets, given the fact that few innovative targets have made it to the approval list each year. Knowledge of existing drug targets (both approved and within clinical trials) is highly useful for facilitating target discovery, selection, exploration and tool development. The Therapeutic Target Database (TTD) has been developed and updated to provide information on 358 successful targets, 251 clinical trial targets and 1254 research targets in addition to 1511 approved drugs, 1118 clinical trials drugs and 2331 experimental drugs linked to their primary targets (3257 drugs with available structure data). This review briefly describes the TTD database and illustrates how its data can be explored for facilitating target and drug searches, the study of the mechanism of multi-target drugs and the development of in silico target discovery tools.     

Potential of metabolomics in preclinical and clinical drug development

Metabolomics is an upcoming technology system which involves detailed experimental analysis of metabolic profiles. Due to its diverse applications in preclinical and clinical research, it became an useful tool for the drug discovery and drug development process. This review covers the brief outline about the instrumentation and interpretation of metabolic profiles. The applications of metabolomics have a considerable scope in the pharmaceutical industry, almost at each step from drug discovery to clinical development. These include finding drug target, potential safety and efficacy biomarkers and mechanisms of drug action, the validation of preclinical experimental models against human disease profiles, and the discovery of clinical safety and efficacy biomarkers. As we all know, nowadays the drug discovery and development process is a very expensive, and risky business. Failures at any stage of drug discovery and development process cost millions of dollars to the companies. Some of these failures or the associated risks could be prevented or minimized if there were better ways of drug screening, drug toxicity profiling and monitoring adverse drug reactions. Metabolomics potentially offers an effective route to address all the issues associated with the drug discovery and development.     

人工智能在药物发现中的应用与挑战

新药研发存在周期长、费用高和成功率低等特点。人工智能技术是近些年来的热点技术之一,在很多领域都有非常广泛的应用,多种人工智能方法已经成功应用于药物的发现过程。综述总结了常用机器学习方法和深度学习方法在药物研发领域中的应用,同时也提出了人工智能存在的问题和面临的挑战。整体而言,人工智能技术在药物研发领域发展潜力巨大,将为医药发展带来新的机遇和希望。     

Use of constraint-based modeling for the prediction and validation of antimicrobial targets

The overall process of antimicrobial drug discovery and development seems simple, to cure infectious disease by identifying suitable antibiotic drugs. However, this goal has been difficult to fulfill in recent years. Despite the promise of the high-throughput innovations sparked by the genomics revolution, discovery, and development of new antibiotics has lagged in recent years exacerbating the already serious problem of evolution of antibiotic resistance. Therefore, both new antimicrobials are desperately needed as are improvements to speed up or improve nearly all steps in the process of discovering novel antibiotics and bringing these to clinical use. Another product of the genomic revolution is the modeling of metabolism using computational methodologies. Genomic-scale networks of metabolic reactions based on stoichiometry, thermodynamics and other physico-chemical constraints that emulate microbial metabolism have been developed into valuable research tools in metabolic engineering and other fields. This constraint-based modeling is predictive in identifying critical reactions, metabolites, and genes in metabolism. This is extremely useful in determining and rationalizing cellular metabolic requirements. In turn, these methods can be used to predict potential metabolic targets for antimicrobial research especially if used to increase the confidence in prioritization of metabolic targets. The many different capacities of constraint-based modeling also enable prediction of cellular response to specific inhibitors such as antibiotics and this may, ultimately find a role in drug discovery and development. Herein, we describe the principles of metabolic modeling and how they might initially be applied to antimicrobial research.     

Clinical-pharmacological aspects to accelerate the development process from the preclinical to the clinical phase/1st communication: The contribution of clinical pharmacology

To improve the transition from research to development a critical evaluation of the individual project by research and disease area teams is required to include input from pharmacology, toxicology, pharmacokinetics, galenics, clinical pharmacology, clinical as well as regulatory experts and marketing. Decisions on the individual development strategy should be made prior to the start of development and all projects should be reviewed at predefined stages throughout the product development life cycle. This ensures consistency of decision-making not only during the development of individual products but throughout the entire development pipeline. Studies in the exploratory stage of drug development should be designed for decision making in contrast to later clinical trials in the confirmatory stage that require power for proof-of-safety and proof-of-efficacy. The more thorough and profound studies have been carried out during this exploratory stage of drug development, the earlier a decision can be made on the continuation or discontinuation of further development, thus saving development time and money and assessing and considerably reducing the risk for the patients and increasing the success rate of the project in the later confirmatory effectiveness trial with an adequate number of subjects receiving the new therapy under typical conditions of use. Strategies which may be helpful to improve the quality of decisions in drug discovery and drug development are: discovery experiments should be done to critically evaluate the compound, the "killer" experiments should be done as early as possible, continuous effort on preclinical disease models is necessary to improve predictability of efficacy in patients ("humanized" research): genomic technology should be used to identify novel, disease-related targets and to characterise preclinical test systems, improvement of knowledge and experience concerning the relevance of new technologies for the clinical picture; genotyping of clinical trial patients to select patient groups which are likely to respond to treatment (pharmacogenomics), modelling and simulation of preclinical and clinical trials, integration of pharmacokinetic and pharmacodynamic principles into drug development, assessment of the interaction potential (CYP-450, trasporter proteins and others), increasing use of biomarker/surrogate marker for rapid clinical feedback, involvement of the target population as soon as possible, applying statistical data analysis techniques for proving effectiveness, co-operation with high quality centers. To reach this goal clinical pharmacology must be fully integrated in the whole process from the candidate selection to its positioning within the market.     

Clinical-pharmacological aspects to accelerate the development process from the preclinical to the clinical phase/2nd communication: promising strategies

To improve the transition from research to development a critical evaluation of the individual project by research and disease area teams is required to include input from pharmacology, toxicology, pharmacokinetics, galenics, clinical pharmacology, clinical as well as regulatory experts and marketing. Decisions on the individual development strategy should be made prior to the start of development and all projects should be reviewed at predefined stages throughout the product development life cycle. This ensures consistency of decision-making not only during the development of individual products but throughout the entire development pipeline. Studies in the exploratory stage of drug development should be designed for decision making in contrast to later clinical trials in the confirmatory stage that require power for proof-of-safety and proof-of-efficacy. The more thorough and profound studies have been carried out during this exploratory stage of drug development, the earlier a decision can be made on the continuation or discontinuation of further development, thus saving development time and money and assessing and considerably reducing the risk for the patients and increasing the success rate of the project in the later confirmatory effectiveness trial with an adequate number of subjects receiving the new therapy under typical conditions of use. Strategies which may be helpful to improve the quality of decisions in drug discovery and drug development are: discovery experiments should be done to critically evaluate the compound, the "killer" experiments should be done as early as possible, continuous effort on preclinical disease models is necessary to improve predictability of efficacy in patients ("humanized" research): genomic technology should be used to identify novel, disease-related targets and to characterise preclinical test systems, improvement of knowledge and experience concerning the relevance of new technologies for the clinical picture, genotyping of clinical trial patients to select patient groups which are likely to respond to treatment (pharmacogenomics), modelling and simulation of preclinical and clinical trials, integration of pharmacokinetic and pharmacodynamic principles into drug development, assessment of the interaction potential (CYP-450, trasporter proteins and others), increasing use of biomarker/surrogate marker for rapid clinical feedback, involvement of the target population as soon as possible, applying statistical data analysis techniques for proving effectiveness, co-operation with high quality centers. To reach this goal clinical pharmacology must be fully integrated in the whole process from the candidate selection to its positioning within the market.