Advancing Drug Innovation for Neglected Diseases—Criteria for Lead Progression

The current drug R&D pipeline for most neglected diseases remains weak, and unlikely to support registration of novel drug classes that meet desired target product profiles in the short term. This calls for sustained investment as well as greater emphasis in the risky upstream drug discovery. Access to technologies, resources, and strong management as well as clear compound progression criteria are factors in the successful implementation of any collaborative drug discovery effort. We discuss how some of these factors have impacted drug discovery for tropical diseases within the past four decades, and highlight new opportunities and challenges through the virtual North–South drug discovery network as well as the rationale for greater participation of institutions in developing countries in product innovation. A set of criteria designed to facilitate compound progression from screening hits to drug candidate selection is presented to guide ongoing efforts.     

Opportunities for pharmacoproteomics in biomarker discovery

Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data-independent acquisition mass spectrometry (DIA-MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high-performance computing. Finally, we explore the value of pre-clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape.     

Human induced pluripotent stem cells derived hepatocytes: rising promise for disease modeling,drug development and cell therapy

Recent advances in the study of human hepatocytes derived from induced pluripotent stem cells (iPSC) represent new promises for liver disease study and drug discovery. Human hepatocytes or hepatocyte-like cells differentiated from iPSC recapitulate many functional properties of primary human hepatocytes and have been demonstrated as a powerful and efficient tool to model human liver metabolic diseases and facilitate drug development process. In this review, we summarize the recent progress in this field and discuss the future perspective of the application of human iPSC derived hepatocytes.     

Synthesis and evaluation of human phosphodiesterases (PDE) 5 inhibitor analogs as trypanosomal PDE inhibitors. Part 1. Sildenafil analogs

Parasitic diseases, such as African sleeping sickness, have a significant impact on the health and well-being in the poorest regions of the world. Pragmatic drug discovery efforts are needed to find new therapeutic agents. In this Letter we describe target repurposing efforts focused on trypanosomal phosphodiesterases. We outline the synthesis and biological evaluation of analogs of sildenafil (1), a human PDE5 inhibitor, for activities against trypanosomal PDEB1 (TbrPDEB1). We find that, while low potency analogs can be prepared, this chemical class is a sub-optimal starting point for further development of TbrPDE inhibitors.     

The Ubiquitination-Dependent and -Independent Functions of Cereblon in Cancer and Neurological Diseases

Cereblon (CRBN) mediates the teratogenic effect of thalidomide in zebrafish, chickens, and humans. It additionally modulates the anti-myeloma effect of the immunomodulatory drugs (IMiDs) thalidomide, lenalidomide, and pomalidomide. IMiDs bind to CRBN and recruit neo-substrates for their ubiquitination and proteasome-mediated degradation, which significantly expands the application of proteolysis-targeting chimeras (PROTACs) for targeted drug discovery. However, the underlying molecular mechanisms by which CRBN mediates the teratogenicity and anti-myeloma effect of IMiDs have not been fully elucidated. Furthermore, the normal physiological functions of endogenous CRBN have not been extensively studied, which prevents the thorough assessment of side effects of the CRBN ligand-based PROTACs in the treatment of cancer and neurological diseases. To advance our understanding of the diverse functions of CRBN, in this review, we will survey the ubiquitination-dependent and -independent functions of CRBN, summarize recent advances in the discovery of constitutive substrates and neo-substrates of CRBN, and explore the molecular functions of CRBN in cancer treatment and in the development of neurological diseases. We will also discuss the potential future directions toward the identification of CRBN substrates/interacting proteins and CRBN ligand-based drug discovery in the treatment of cancer and neurological diseases.     

Using NMR for ligand discovery and optimization

Several recent technology-driven advances in the area of NMR have rekindled an interest in the application of the technology to problems in drug discovery and development. A unique aspect of NMR is that it has applicability in broadly different areas of the drug discovery and optimization processes. NMR techniques for screening aimed at the discovery of novel ligands or low molecular weight structures for fragment-based build up procedures are being applied commonly in the industry. Application of NMR in structure-guided drug design and metabonomics are also becoming routine. We present an overview of some of the most recent NMR developments in these areas.     

Serendipity and new drugs for infectious disease

Serendipity, in various shades of semantic legitimacy, is abundantly evident in the history of the chemotherapy of infectious disease. We may be on the threshold of a new era of rational drug design, but most medications for infectious diseases have arisen, and continue to arise, from chance observation, clinical experience, and the empirical search for substances active against pathogens. Chance does not produce drugs; but where chance has played a pivotal role in drug discovery, the event may be considered serendipitous to a greater or lesser degree. In a deliberate search for new drugs, it is often difficult to assess the degree to which any resulting discovery is serendipitous, and the usefulness of the term becomes debatable. Many therapeutic advances emerge from research involving animals, and a triggering "happy accident" may reside in the most basic aspects of animal care or in the most arcane knowledge of animals. The examples discussed in this article deal mostly with parasitic disease and the use of animal models in the discovery of antiparasitic agents. In this area, as in others, chance has laid the groundwork for scientific advancement and practical benefit. Although the applicability of the word serendipity to drug discovery may often be uncertain, the role played by chance should be recognized and welcomed.     

Analysis of phase stability,elastic, electronic,thermal, and optical properties of Sc1-xYxN via ab initio methods

Journal of Molecular Modeling - The recent advances in the application of machine learning to drug discovery have made it a ‘hot topic’ for research, with hundreds of academic groups...     

Target-based drug discovery,genetic diseases,and biologics

The last fifteen years have witnessed a major strategic shift in drug discovery away from an empiric approach based on incremental improvements of proven therapies, to a more theoretical, target-based approach. This arose as a consequence of three technical advances: (1) generation and interpretation of genome sequences, which facilitated identification and characterization of potential drug targets; (2) efficient production of candidate ligands for these putative targets through combinatorial chemistry or generation of monoclonal antibodies; and (3) high-throughput screening for rapid evaluation of interactions of these putative ligands with the selected targets. The basic idea underlying all three of these technologies is in keeping with Marshall Nirenberg’s dictum that science progresses best when there are simple assays capable of generating large data sets rapidly. Furthermore, practical implementation of target-based drug discovery was enabled directly by technologies that either were originated or nurtured by Marshall, his post-docs and fellows. Chief among these was the genetic code. Also important was adoption of clonal cell lines for pharmacological investigations, as well as the use of hybridomas to generate molecular probes that allowed physical purchase on signaling elements that had previously been only hypothetical constructs. Always the pure scientist, Marshall’s contributions nevertheless enabled fruitful applications in the pharmaceutical industry, several of them by his trainees. Both the successes and the shortcomings of target-based drug discovery are worthy of consideration, as are its implications for the choices of therapeutic goals and modalities by the pharmaceutical industry.     

Leveraging the Genetic Diversity of Human Stem Cells in Therapeutic Approaches

Since their discovery 15 years ago, human pluripotent stem cell (hPSC) technologies have begun to revolutionize science and medicine, rapidly expanding beyond investigative research to drug discovery and development. Efforts to leverage hPSCs over the last decade have focused on increasing both the complexity and in vivo fidelity of human cellular models through enhanced differentiation methods. While these evolutions have fostered novel insights into disease mechanisms and influenced clinical drug discovery and development, there are still several considerations that limit the utility of hPSC models. In this review, we highlight important, yet underexplored avenues to broaden their reach. We focus on (i) the importance of diversifying existing hPSC collections, and their utilization to investigate therapeutic strategies in individuals from different genetic backgrounds, ancestry and sex; (ii) considerations for the selection of therapeutically relevant hPSC-based models; (iii) strategies to adequately increase the scale of cell-based studies; and (iv) the advances and constraints of clinical trials in a dish. Moreover, we advocate for harnessing the translational capabilities of hPSC models along with the use of innovative, scalable approaches for understanding genetic biases and the impact of sex and ancestry on disease mechanisms and drug efficacy and response. The next decade of hPSC innovation is poised to provide vast insights into the genetic basis of human disease and enable rapid advances to develop, repurpose, and ensure the safety of the next generation of disease therapies across diverse human populations.     

A flexible technology platform to explore valuable drug targets

The high-throughput screening platform implemented for drug discovery is driven by the therapeutic areas of interest. Therefore the speed and information derived is governed by these areas. Multiple technologies are needed to exploit this and it is also important to show reactivity to new advances in technology. In contrast with a drive over the last few years towards higher throughput and speed, higher information content will be instrumental in driving lead discovery in the future.     

Applied genome research in the field of human vaccines

Prophylactic vaccination has made an essential contribution to the improvement of human health over the 20th century. However, we still lack efficient vaccines against major human diseases such as malaria or tuberculosis. Today, the design of therapeutic vaccines referred to as 'pharmaccines' is actively investigated in order to treat diseases such as cancer. In that context, novel ways to rationalize and accelerate vaccine discovery are needed. A series of advances in the fields of molecular biology and computer science, have greatly accelerated the rate at which candidate vaccine antigens can be discovered. In this review, we will present and discuss how applied genome research may facilitate antigen discovery and the design of new prophylactic and therapeutic vaccines.     

放线菌来源活性天然产物发现研究进展

放线菌(Actinomycetes)是抗生素等活性天然产物的重要来源，在临床治疗病原菌感染等重大疾病方面发挥着重要作用。由于抗生素滥用等原因导致临床上出现的以耐甲氧西林金黄色葡萄球菌(methicillin-resistant Staphylococcus aureus, MRSA)等为代表的耐药病菌的种类和数量急剧增加，对人类生存造成了重大威胁。结合现代生物技术进展，加大放线菌来源新抗生素的开发力度刻不容缓。本文对新时期放线菌来源抗生素的发现现状、基于生理操作和基因操作的放线菌来源天然产物挖掘的技术方法等进行综述，以期对放线菌来源活性天然产物的发现提供借鉴。     

Proteomics approaches to urologic diseases

Biomarkers are greatly needed for several urologic diseases, such as interstitial cystitis, the symptomatic and clinical progression of benign prostate hyperplasia, as well as the specific detection of urologic cancers, including prostate and bladder cancer. This review aims to: briefly describe the need for biomarkers in the field and biomarkers that are currently available for clinicians; address the limitations and roadblocks to effective biomarker discovery; and provide examples and strategies for implementing biomarkers in clinical practice and/or drug discovery.     

Progress in functional genomics approaches to antifungal drug target discovery

Antifungal drug discovery is starting to benefit from the enormous advances in the genomics field, which have occurred in the past decade. As traditional drug screening on existing targets is not delivering the long-awaited potent antifungals, efforts to use novel genetics and genomics-based strategies to aid in the discovery of novel drug targets are gaining increased importance. The current paradigm in antifungal drug target discovery focuses on basically two main classes of targets to evaluate: genes essential for viability and virulence or pathogenicity factors. Here we report on recent advances in genetics and genomics-based technologies that will allow us not only to identify and validate novel fungal drug targets, but hopefully in the longer run also to discover potent novel therapeutic agents. Fungal pathogens have typically presented significant obstacles when subjected to genetics, but the creativity of scientists in the anti-infectives field and the cross-talk with scientists in other areas is now yielding exciting new tools and technologies to tackle the problem of finding potent, specific and non-toxic antifungal therapeutics.     

Fishing for causes and cures of motor neuron disorders

Motor neuron disorders (MNDs) are a clinically heterogeneous group of neurological diseases characterized by progressive degeneration of motor neurons, and share some common pathological pathways. Despite remarkable advances in our understanding of these diseases, no curative treatment for MNDs exists. To better understand the pathogenesis of MNDs and to help develop new treatments, the establishment of animal models that can be studied efficiently and thoroughly is paramount. The zebrafish (Danio rerio) is increasingly becoming a valuable model for studying human diseases and in screening for potential therapeutics. In this Review, we highlight recent progress in using zebrafish to study the pathology of the most common MNDs: spinal muscular atrophy (SMA), amyotrophic lateral sclerosis (ALS) and hereditary spastic paraplegia (HSP). These studies indicate the power of zebrafish as a model to study the consequences of disease-related genes, because zebrafish homologues of human genes have conserved functions with respect to the aetiology of MNDs. Zebrafish also complement other animal models for the study of pathological mechanisms of MNDs and are particularly advantageous for the screening of compounds with therapeutic potential. We present an overview of their potential usefulness in MND drug discovery, which is just beginning and holds much promise for future therapeutic development.KEY WORDS: ALS, HSP, SMA, Zebrafish, Drug discovery, Motor neuron disorders     

Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery

Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein‐ligand interactions between 296 proteins spanning Escherichia coli''s essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning‐based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true‐positive rate to false‐positive rate. This work indicates that advances in modeling protein‐ligand interactions, particularly using machine learning‐based approaches, are needed to better harness AlphaFold2 for drug discovery.