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PubMed2001

Role of modelling and simulation in Phase I drug development.

Although the use of pharmacokinetic/pharmacodynamic modelling and simulation (M&S) in drug development has increased during the last decade, this has most notably occurred in patient studies using the population approach. The role of M&S in Phase I, although of longer history, does not presently have the same impact on drug development. However, trends such as the increased use of biomarkers and clinical trial simulation as well as adoption of the learn/confirm concept can be expected to increase the importance of modelling in Phase I. To help identify the role of M&S, its main advantages and the obstacles to its rational use, an expert meeting was organised by COST B15 in Brussels, January 10-11, 2000. This article presents the views expressed at that meeting. Although it is clear that M&S occurs in only a minority of Phase I clinical trials, it is used for a large number of different purposes. In particular, M&S is considered valuable in the following situations: censoring because of assay limitation, characterisation of non-linearity, estimating exposure-response relationship, combined analyses, sparse sampling studies, special population studies, integrating PK/PD knowledge for decision making, simulation of Phase II trials, predicting multiple dose profile from single dose, bridging studies and formulation development. One or more of the following characteristics of M&S activities are often present and severely impede its successful integration into clinical drug development: lack of trained personnel, lack of protocol and/or analysis plan, absence of pre-specified objectives, no timelines or budget, low priority, inadequate reporting, no quality assurance of the modelling process and no evaluation of cost-benefit. The early clinical drug development phase is changing and if these implementation aspects can be appropriately addressed, M&S can fulfill an important role in reshaping the early trials by more effective extraction of information from studies, better integration of knowledge across studies and more precise predictions of trial outcome, thereby allowing more informed decision making.

Clinical Trials, Phase I as Topic↗

PubMed2002

Toxicogenomics: an opportunity to optimise drug development and safety evaluation.

'Toxicogenomics', the use of complex populations of mRNA to understand toxicity, is a relatively new field which combines the wealth of gene sequence information with advances in miniaturisation technology. In a parallel evolutionary path that the broader field of toxicology has encountered, toxicogenomics is steadily changing from descriptive to mechanistic research and will ultimately progress to a predictive science. This review focuses on the application of microarray research tools to toxicology studies in preclinical development. In particular, the 'testing funnel' approach to candidate selection in drug development is used to discuss the strategic implementation of toxicogenomics to help develop drugs that harbour less toxicity. Examples of where toxicogenomics has increased mechanistic and descriptive understanding of cellular toxicity are discussed, as are early efforts to develop molecular biomarkers that are predictive of longer-term toxicity.

Animals↗

PubMed2002

The value of improving the productivity of the drug development process: faster times and better decisions.

OBJECTIVES: This study examines the financial benefits that can accrue to drug developers from improvements in the drug development process. The effect on drug development costs from faster development, earlier decisions on project failures, and higher approval success rates are quantified. DATA AND METHODS: The results from a recent study of research and development (R&D) costs for new drugs are used as a benchmark against which improvements in the discovery and development processes are simulated. The cost results in the benchmark study were based on a sample of 68 randomly selected investigational drugs from 10 pharmaceutical firms. RESULTS: Simultaneous 25% reductions in phase lengths lower capitalized total cost per approved drug by 16%, or $US129 million; 50% reductions in time lower cost by 29%, or $US235 million. Earlier decisions to terminate research on drugs that will ultimately fail significantly reduce clinical costs. For example, shifting 5% of all clinical failures from phase III/regulatory review to phase I reduces out-of-pocket clinical costs by 5.5 to 7.1%; and capitalized clinical cost is lowered by 5.1 to 6.3%. If more productive discovery programmes or better preclinical screens increase success rates from 21.5% to one in three, firms can reduce capitalized total cost per approved drug by $US221 million to $US242 million. CONCLUSIONS: Whether faster development times, quicker termination decisions or higher success rates derive from public policy initiatives, better management, or new technologies, the impact on R&D costs can be substantial. Ultimately, the increased efficiency could result in more innovation and new therapies reaching patients sooner.

Benchmarking↗

PubMed2002

An introduction to clinical research and drug development for pharmacy and pharmacology graduate students.

Recently at the University of Houston College of Pharmacy, a new course was introduced to provide an overview of each phase of drug development research, from the initial chemistry assays and cell biologystudies to animal experiments and finally clinical trials for drug approval. The course, entitled "Clinical Research and Drug Development," is a three-credit elective introduced this past yearfor the entry-level Doctor of Pharmacy (PharmD) students and graduate students during the summer session at the college. Overall, the intent of this course is to develop students' interest in the topic of clinical research and drug development and provide a foundation of knowledge of basic research skills used in the drug development process. The approach for this course was to break the topics into three general modules: laboratory techniques, both analytical and cell biology/molecular, used in the drug development process; aspects of clinical research process; and writing grants and protocols. Throughout the course, students were strongly encouraged to consider pursuing independent research projects to help continue to develop their research skills in preparation for postgraduate training. As a result, on completion of the course, four students requested an opportunity to complete an independent research project. This course has introduced the various components of drug development and of conducting clinical research to students in the PharmD and graduate programs at the University of Houston College of Pharmacy.

Biomedical Research↗

PubMed2000

An evolution of drug development and clinical pharmacology during the 20th century.

The current state of clinical pharmacology and drug development did not just happen. Clinical pharmacology and the drug development process were born, evolved, and have come to the fore during the past 100 years. The past century has been one of accelerating progress in science and medicine. The progress has not been a straight line but rather more like a sidewinder moving across the desert. Drug development has moved from small experiments with concoctions, extracts, and potions along with the manufacturing and promotion of the purported remedies to processes that exploited unknowing patients to a process that now requires concept generation, discovery, research, planning, and development with many checks and balances for the protection of human subjects. The factors that contributed to this progression from inappropriate use of potions, concoctions, and snake oil to the highly regulated drug development process of today is described in some detail.

Drug Design↗

PubMedDate not supplied

Biomarkers in psychotropic drug development.

The authors review the use of biomarkers in the development of novel psychotropic agents. They briefly review clinical drug development, emphasizing the importance of incorporating biomarkers. For the development of psychotropic agents, biomarkers are particularly useful for assessing central nervous system exposure and effects and for serving as surrogate measures for safety and efficacy. Collectively, biomarkers allow for more accurate estimation of doses for clinical trials as drug development progresses. For drugs that target the pathophysiology of Alzheimer disease, several promising biomarkers are becoming available that may allow improved signal detection in clinical trials. Procedures for developing new drugs are evolving rapidly. Technical advances in the field are making it possible to shift from empirically-based methods to mechanistically-driven schemes. Biomarkers enhance the quality and safety of clinical drug development and reduce its cost and duration.

Aged↗

PubMed2006

Model-based drug development: the road to quantitative pharmacology.

High development costs and low success rates in bringing new medicines to the market demand more efficient and effective approaches. Identified by the FDA as a valuable prognostic tool for fulfilling such a demand, model-based drug development is a mathematical and statistical approach that constructs, validates, and utilizes disease models, drug exposure-response models, and pharmacometric models to facilitate drug development. Quantitative pharmacology is a discipline that learns and confirms the key characteristics of new molecular entities in a quantitative manner, with goal of providing explicit, reproducible, and predictive evidence for optimizing drug development plans and enabling critical decision making. Model-based drug development serves as an integral part of quantitative pharmacology. This work reviews the general concept, basic elements, and evolving role of model-based drug development in quantitative pharmacology. Two case studies are presented to illustrate how the model-based drug development approach can facilitate knowledge management and decision making during drug development. The case studies also highlight the organizational learning that comes through implementation of quantitative pharmacology as a discipline. Finally, the prospects of quantitative pharmacology as an emerging discipline are discussed. Advances in this discipline will require continued collaboration between academia, industry and regulatory agencies.

Adult↗

PubMed2002

Applications of antisense and siRNAs during preclinical drug development.

A significantly greater number of candidate drug targets and compounds are now being generated during preclinical drug development. To date, however, such increases have not led to improvements in clinical success rates or reduced times to market. There is a need for better strategies to prioritize targets and drug candidates. Antisense and siRNA technologies offer exceptional speed and specificity to address this need. In particular, antisense and siRNAs are beginning to be used in combination with expression profiling to evaluate drug specificity and mechanism-of-action, aiding in the identification of better candidates earlier in the drug development process.

Drug Evaluation, Preclinical↗

PubMed2005

Novel targets for cardiac antiarrhythmic drug development.

Cardiac arrhythmias remain a major source of morbidity and mortality in developed countries. Antiarrhythmic drug therapy was traditionally the mainstay of cardiac arrhythmia treatment; however, drug therapy of cardiac arrhythmias has been plagued by incomplete efficacy and by potentially serious adverse reactions, of which the most worrisome has been a potential for malignant proarrhythmia and related effects to increase cardiac mortality. This article reviews the principal arrhythmia mechanisms and their ionic determinants, and discusses potential innovative approaches to new antiarrhythmic drug development, including the consideration of novel ionic targets, potential biophysical approaches and non-channel components involved in composing the arrhythmic substrate.

Action Potentials↗

PubMed2004

Progress in applying genomics in drug development.

Genomics has had an impact on two areas of drug development, "predictive" toxicology and mechanism-based risk assessment. Predictive toxicology studies are aimed at identifying the potential for a compound to be toxic. By developing databases of expression profiles for a wide variety of toxic compounds and toxic models it has been possible to create statistical and computational methods which provide an indication of the toxic potential of a drug from the pattern of gene expression changes it elicits in in vitro or in vivo systems. Because gene expression is central to many responses to xenobiotics, genomic approaches lend themselves very readily to mechanistic toxicology studies. By examining changes in gene expression in cells and tissues in response to drugs it is possible to generate hypotheses as to the underlying mechanism and in some cases it is possible to evaluate hypotheses of toxic mechanism. Some concerns remain about the use of the technology but toxicogenomics can no longer be regarded as "new" technology in drug development. The investments made in applying the technology are maturing and there is a determined effort to bring the full power of the technology into drug development.

Animals↗

PubMed2006

A strategic view on the use of pharmacodynamic biomarkers in early clinical drug development.

Early-phase clinical trials have traditionally focused on the safety, tolerability and pharmacokinetic properties of a therapeutic candidate, while questions regarding efficacy have been left unanswered until phase II clinical development. Although an understanding of all of these parameters is unarguably essential to the successful development of a drug candidate, this information is insufficient. Biomarkers are increasingly being used in early drug development to allow for the exploration of pharmacodynamic effects of targeted therapeutics before clinical signs and symptoms are evaluated. The trend of using pharmacodynamic biomarkers will continue and, ultimately, may enable these biomarkers to be used routinely to demonstrate the impact of a compound on the treatment of a disease, and to aid in the selection of therapeutic doses and regimens based on accurate measures of human physiological responses. Biomarkers are also beginning to be used to determine those patients who are most likely to respond to a therapeutic, and thereby improve treatment outcomes while avoiding the administration of a drug to individuals who are unlikely to benefit. This article describes a strategy that is being used to achieve these aims for biomarkers, and discusses how biomarkers can be used to identify information that can be applied back into to the drug discovery process.

Biomarkers↗

PubMed2007

Exploratory IND: a new regulatory strategy for early clinical drug development in the United States.

Optimizing exploratory drug development by means of doing first-in-human studies earlier is an attractive option for pharmaceutical sponsors to select more successful drug candidates. Traditional registration-driven clinical phase 1 programs could be preceded by early human screening studies with subpharmacological single doses (microdoses) or with low pharmacologically active doses of one or several lead candidates, whereby human pharmacokinetic and pharmacodynamic data are generated very early. Such low doses are not expected to have clinically significant toxic potential, so early human screening studies may be supported by abbreviated nonclinical safety packages. Recent U.S. FDA draft guidance (April 2005) regulated early human screening studies conducted under the exploratory IND. The author outlines the features of the study design, dose selection, and nonclinical safety packages required in support of exploratory IND studies in humans. In appropriately chosen cases, exploratory IND could allow for patients' quicker access to safer and more efficacious doses of novel drugs, reduce attrition in clinical trials, and facilitate more economical drug development.

Animals↗

PubMed2005

The impact of tumor physiology on camptothecin-based drug development.

The genomic era has shifted anticancer drug development from its traditional mode concentrated on natural product cytotoxic agents to mechanism-based drug design focused on signal transduction pathways. Yet traditional cytotoxic chemotherapies continue to have an important role in the armamentarium. This is particularly true when one considers that important elements of solid tumor physiology - acidosis and hypoxia - have rarely been incorporated into algorithms for anticancer drug development. It is now well established that a majority of solid tumors exist in an acidic and hypoxic microenvironment that promotes resistance to radiation and chemotherapies apart from any drug-induced target mutations or efflux protein pumps. The acidic extracellular environment leads to a pH gradient unique to tumor cells. This gradient will favor uptake and retention of small molecule drugs that are weak acids. The converse is true for weak bases. The camptothecin class of topoisomerase I inhibitors is one example of a natural product cytotoxic that can exploit the tumor pH gradient. Screening of compounds based on selective activity at acidic pH (pH modulation), rather than potency, reveals analogs that are over ten times more active under the acidic conditions prevalent in vivo. Thus, knowledge of the tumor metabolic phenotype gained at the beginning of the 20(th) century can lead to more effective anticancer drugs in the new millennium.

Acidosis↗

PubMed2026

The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.

Natural products (NPs) have historically provided the foundational scaffolds for drug development, yet traditional bioprospecting faces critical limitations: high rediscovery rates, laborious isolation workflows, and substantial attrition during clinical translation. The emergence of big data technologies is fundamentally transforming this landscape, enabling a shift from serendipity-based discovery toward systematic, data-driven approaches. This review examines how the integration of artificial intelligence (AI), machine learning (ML), and multi-omics datasets is accelerating natural product research across three key domains: (1) genome mining for biosynthetic gene cluster identification using platforms such as antiSMASH, (2) cheminformatics-driven prediction of structure-activity relationships and ADMET properties, and (3) metabolomics-guided dereplication to prioritize novel bioactive scaffolds. We evaluate the convergence of genomics, metabolomics, and computational chemistry in enabling in silico lead optimization and the discovery of cryptic metabolites from previously inaccessible microbial taxa. While challenges in data standardization and scalability persist, the synergy between big data and NP research is accelerating clinical translation. Despite persistent challenges in data standardization, scalability, and equitable benefit-sharing, the convergence of big data and NP research is poised to redefine drug development. These advances position computational NP research as a cornerstone of next-generation drug development.

big data analytics↗

PubMed1995

Rare diseases, drug development, and AIDS: the impact of the Orphan Drug Act.

The Orphan Drug Act provides public subsidies and incentives to spur the development of drugs for rare diseases--drugs that the private sector might otherwise consider unprofitable to produce. Although the act has achieved numerous successes, the high prices and extraordinary sales generated by some orphan drugs lead to a pivotal policy question: how can the act be used to meet the legislative goal of stimulating drug development for small patient populations without resulting in prices that make drugs inaccessible? This question is explored using the example of AIDS drugs, many of which received subsidies under the act, to illustrate central points. The history of the act, its weaknesses, and strategies for reform are described as well.

AIDS-Related Opportunistic Infections↗

PubMed2005

An integrated approach for inference and mechanistic modeling for advancing drug development.

An important challenge facing researchers in drug development is how to translate multi-omic measurements into biological insights that will help advance drugs through the clinic. Computational biology strategies are a promising approach for systematically capturing the effect of a given drug on complex molecular networks and on human physiology. This article discusses a two-pronged strategy for inferring biological interactions from large-scale multi-omic measurements and accounting for known biology via mechanistic dynamical simulations of pathways, cells, and organ- and tissue level models. These approaches are already playing a role in driving drug development by providing a rational and systematic computational framework.

Algorithms↗

PubMedDate not supplied

Weaving single photon imaging into new drug development.

The specific aim of this review is to assess the potential contribution of single photon emitting radiopharmaceutical technologies to new drug development. For each phase of therapeutic drug development, published literature was sought that shows single photon emitters can add value by quantifying pharmacokinetics, visualizing mechanisms of drug action, estimating therapeutic safety indices, or measuring dose-dependent pharmacodynamic effects. Not any published reports were found that describe using nuclear medicine techniques to help manage the progress of a new drug development program. As a consequence, most of the case in favor of weaving single photon imaging into the process had to be built on extrapolations from studies that showed feasibility post hoc. The strongest evidence of potential value was found for drug candidates that hope to influence diseases characterized by cell proliferation or cell death, particularly in the fields of oncology, cardiology, nephrology, and inflammation. Receptor occupancy studies were observed to occasionally offer unique advantages over analogous studies with positron emission tomography (PET). Enough hard data sets were found to justify the costs of using single photon imaging in a variety of new drug development paradigms.

Animals↗

PubMed2004

Molecular biomarkers in drug development.

Arguably, the most immediately promising reverberation of the genomics era has been the application of biomarkers to drug development. The promise of applying biomarkers to early drug development is that they might aid in preclinical and early clinical decisions such as dose ranging, definition of treatment regimen, or even a preview of efficacy. Later in the clinic, biomarkers could be used to facilitate patient stratification, selection and the description of surrogate endpoints. Information derived from biomarkers should result in a better understanding of preclinical and clinical data, which ultimately benefits patients and drug developers. If the promise of biomarkers is realized, they will become a routine component of drug development and companions to newly discovered therapies.

Animals↗