Explore the vast range of titles available.

The large-scale genetic profiling of tumours can identify potentially actionable molecular variants for which approved anticancer drugs are available 1 – 3 . However, when patients with such variants are treated with drugs outside of their approved label, successes and failures of targeted therapy are not systematically collected or shared. We therefore initiated the Drug Rediscovery protocol, an adaptive, precision-oncology trial that aims to identify signals of activity in cohorts of patients, with defined tumour types and molecular variants, who are being treated with anticancer drugs outside of their approved label. To be eligible for the trial, patients have to have exhausted or declined standard therapies, and have malignancies with potentially actionable variants for which no approved anticancer drugs are available. Here we show an overall rate of clinical benefit—defined as complete or partial response, or as stable disease beyond 16 weeks—of 34% in 215 treated patients, comprising 136 patients who received targeted therapies and 79 patients who received immunotherapy. The overall median duration of clinical benefit was 9 months (95% confidence interval of 8–11 months), including 26 patients who were experiencing ongoing clinical benefit at data cut-off. The potential of the Drug Rediscovery protocol is illustrated by the identification of a successful cohort of patients with microsatellite instable tumours who received nivolumab (clinical benefit rate of 63%), and a cohort of patients with colorectal cancer with relatively low mutational load who experienced only limited clinical benefit from immunotherapy. The Drug Rediscovery protocol facilitates the defined use of approved drugs beyond their labels in rare subgroups of cancer, identifies early signals of activity in these subgroups, accelerates the clinical translation of new insights into the use of anticancer drugs outside of their approved label, and creates a publicly available repository of knowledge for future decision-making. Clinical benefit was observed in 34% of a cohort of 215 patients with cancer who received treatment with anticancer drugs outside of their approved label, in the Drug Rediscovery protocol trial.

\"This book overviews chiral drugs and their impact in the pharmaceutical industry. The chapters detail basics and trends in chiral drug discovery and development, as well as techniques / skills and trends of asymmetric synthesis. The first section introduces the general concept of chirality and its impact on the drug discovery and development. This part includes history of chiral drug development, key technologies for preparation of chiral drugs, and industrial applications of chiral technologies\"--Provided by publisher.

Sets forth the history, state of the science, and future directions of drug discovery Edited by Jie Jack Li and Nobel laureate E. J. Corey, two leading pioneers in drug discovery and medicinal chemistry, this book synthesizes great moments in history, the current state of the science, and future directions of drug discovery into one expertly written and organized work. Exploring all major therapeutic areas, the book introduces readers to all facets and phases of drug discovery, including target selection, biological testing, drug metabolism, and computer-assisted drug design. Drug Discovery features chapters written by an international team of pharmaceutical and medicinal chemists. Contributions are based on a thorough review of the current literature as well as the authors' firsthand laboratory experience in drug discovery. The book begins with the history of drug discovery, describing groundbreaking moments in the field. Next, it covers such topics as: * Target identification and validation * Drug metabolism and pharmacokinetics * Central nervous system drugs * In vitro and in vivo assays * Cardiovascular drugs * Cancer drugs Each chapter features a case study, helping readers understand how science is put into practice throughout all phases of drug discovery. References at the end of each chapter serve as a gateway to groundbreaking original research studies and reviews in the field. Drug Discovery is ideal for newcomers to medicinal chemistry and drug discovery, providing a comprehensive overview of the field. Veterans in the field will also benefit from the perspectives of leading international experts in all aspects of drug discovery.

Digital twins represent a key technology for precision health. Medical digital twins consist of computational models that represent the health state of individual patients over time, enabling optimal therapeutics and forecasting patient prognosis. Many health conditions involve the immune system, so it is crucial to include its key features when designing medical digital twins. The immune response is complex and varies across diseases and patients, and its modelling requires the collective expertise of the clinical, immunology, and computational modelling communities. This review outlines the initial progress on immune digital twins and the various initiatives to facilitate communication between interdisciplinary communities. We also outline the crucial aspects of an immune digital twin design and the prerequisites for its implementation in the clinic. We propose some initial use cases that could serve as “proof of concept” regarding the utility of immune digital technology, focusing on diseases with a very different immune response across spatial and temporal scales (minutes, days, months, years). Lastly, we discuss the use of digital twins in drug discovery and point out emerging challenges that the scientific community needs to collectively overcome to make immune digital twins a reality.

BackgroundTrypanosoma brucei is the causative agent of Human African Trypanosomiasis (also known as sleeping sickness), a disease causing serious neurological disorders and fatal if left untreated. Due to its lethal pathogenicity, a variety of treatments have been developed over the years, but which have some important limitations such as acute toxicity and parasite resistance. Metabolomics is an innovative tool used to better understand the parasite’s cellular metabolism, and identify new potential targets, modes of action and resistance mechanisms. The metabolomic approach is mainly associated with robust analytical techniques, such as NMR and Mass Spectrometry. Applying these tools to the trypanosome parasite is, thus, useful for providing new insights into the sleeping sickness pathology and guidance towards innovative treatments.Aim of reviewThe present review aims to comprehensively describe the T. brucei biology and identify targets for new or commercialized antitrypanosomal drugs. Recent metabolomic applications to provide a deeper knowledge about the mechanisms of action of drugs or potential drugs against T. brucei are highlighted. Additionally, the advantages of metabolomics, alone or combined with other methods, are discussed.Key scientific concepts of reviewCompared to other parasites, only few studies employing metabolomics have to date been reported on Trypanosoma brucei. Published metabolic studies, treatments and modes of action are discussed. The main interest is to evaluate the metabolomics contribution to the understanding of T. brucei’s metabolism.

We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform to infer homology of drug behaviour at a proteomic level by constructing and analysing structural compound-proteome interaction signatures of 3,733 compounds with 48,278 proteins in a shotgun manner. We applied the CANDO platform to predict putative therapeutic properties of 428 psychoactive compounds that belong to the phenylethylamine, tryptamine, and cannabinoid chemical classes for treating mental health indications. Our findings indicate that these 428 psychoactives are among the top-ranked predictions for a significant fraction of mental health indications, demonstrating a significant preference for treating such indications over non-mental health indications, relative to randomized controls. Also, we analysed the use of specific tryptamines for the treatment of sleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. Our innovative use of the CANDO platform may guide the identification and development of novel therapies for mental health indications and provide an understanding of their causal basis on a detailed mechanistic level. These predictions can be used to provide new leads for preclinical drug development for mental health and other neurological disorders.

The economics of antibiotics can be improved by infectious diseases-specific clinical trial networks. While developers would still need to implement an independent phase 1 program as well as studies focused on highly resistant pathogens, standardized procedures in a network focused on usual drug resistance phenotype isolates would permit sharing of controls and would predictably generate high-quality pivotal data for product registration while creating cost and time savings in the range of 30%–40%. This would reduce economic barriers to antibiotic development and contribute to public health.

Background The standard 6-month four-drug regimen for the treatment of drug-sensitive tuberculosis has remained unchanged for decades and is inadequate to control the epidemic. Shorter, simpler regimens are urgently needed to defeat what is now the world’s greatest infectious disease killer. Methods We describe the Phase IIC Selection Trial with Extended Post-treatment follow-up (STEP) as a novel hybrid phase II/III trial design to accelerate regimen development. In the Phase IIC STEP trial, the experimental regimen is given for the duration for which it will be studied in phase III (presently 3 or 4 months) and patients are followed for clinical outcomes of treatment failure and relapse for a total of 12 months from randomisation. Operating characteristics of the trial design are explored assuming a classical frequentist framework as well as a Bayesian framework with flat and sceptical priors. A simulation study is conducted using data from the RIFAQUIN phase III trial to illustrate how such a design could be used in practice. Results With 80 patients per arm, and two (2.5 %) unfavourable outcomes in the STEP trial, there is a probability of 0.99 that the proportion of unfavourable outcomes in a potential phase III trial would be less than 12 % and a probability of 0.91 that the proportion of unfavourable outcomes would be less than 8 %. With six (7.5 %) unfavourable outcomes, there is a probability of 0.82 that the proportion of unfavourable outcomes in a potential phase III trial would be less than 12 % and a probability of 0.41 that it would be less than 8 %. Simulations using data from the RIFAQUIN trial show that a STEP trial with 80 patients per arm would have correctly shown that the Inferior Regimen should not proceed to phase III and would have had a high chance (0.88) of either showing that the Successful Regimen could proceed to phase III or that it might require further optimisation. Conclusions Collection of definitive clinical outcome data in a relatively small number of participants over only 12 months provides valuable information about the likelihood of success in a future phase III trial. We strongly believe that the STEP trial design described herein is an important tool that would allow for more informed decision-making and accelerate regimen development.

Background Parkinson’s disease is a disorder growing in prevalence, disability, and deaths. Healthcare databases provide a ‘real-world’ perspective for millions of individuals. We envisioned helping accelerate drug discovery by using these databases. Objectives The objectives of this study were to assess the association of marketed medications with the risk of parkinsonism in four US claims databases and to evaluate the consistency of the association of β-adrenoreceptor modulation with parkinsonism. Methods The study was conducted using a self-controlled cohort design in which subjects served as their own control. The time from treatment initiation until discontinuation or end of observation was the exposed period and a similar time preceding medication was the unexposed period. Medications were studied at ingredient and class level. The incidence rate ratio (IRR) and combined IRR were calculated. Results We assessed 2181 drugs and 117,015,066 people. Diphenhydramine, isradipine, methylphenidate, armodafinil, and modafinil were associated with reduced risk for parkinsonism in at least two databases. Armodafinil, modafinil, methylphenidate, and the β-agonist albuterol were associated with a 56%, 54%, 39%, and 17% reduction in the risk of having parkinsonism, respectively. Isradipine results were heterogeneous and no significant association was found. Propranolol was associated with a 32% increased risk, the only β-adrenoceptor antagonist (β-blocker) associated with an increased risk. Conclusions Armodafinil, modafinil, and methylphenidate were associated with a decreased risk of parkinsonism, as were β-agonists. Of the β-blockers, only propranolol was associated with increased risk. Healthcare database analyses that incorporate scientific rigor provide insight and direction for drug discovery efforts. These findings show association not causality; however, they offer considerable support to the association between β-adrenergic receptor modulation and risk of Parkinson’s disease.