The druggable genome: Twenty years later

Affordable mRNA Novel Proteins, Recombinant Protein Conversions, and Biosimilars-Advice to Developers and Regulatory Agencies

mRNA technology can replace the expensive recombinant technology for every type of protein, making biological drugs more affordable. It can also expedite the entry of new biological drugs, and copies of approved mRNA products can be treated as generic or biosimilar products due to their chemical nature. The introduction of hundreds of new protein drugs have been blocked due to the high cost of recombinant development. The low CAPEX and OPEX associated with mRNA technology bring it within the reach of developing countries that are currently deprived of life-saving biological drugs. In this paper, we advise developers to introduce novel proteins and switch recombinant manufacturing to mRNA delivery, and we further advise regulatory authorities to allow for the approval of copies of mRNA products with less testing. We anticipate that mRNA technology will make protein drugs, such as natural and engineered proteins, monoclonal antibodies, and vaccines, accessible to billions of patients worldwide.

Tackling Undruggable Targets with Designer Peptidomimetics and Synthetic Biologics

The development of potent, specific, and pharmacologically viable chemical probes and therapeutics is a central focus of chemical biology and therapeutic development. However, a significant portion of predicted disease-causal proteins have proven resistant to targeting by traditional small molecule and biologic modalities. Many of these so-called "undruggable" targets feature extended, dynamic protein-protein and protein-nucleic acid interfaces that are central to their roles in normal and diseased signaling pathways. Here, we discuss the development of synthetically stabilized peptide and protein mimetics as an ever-expanding and powerful region of chemical space to tackle undruggable targets. These molecules aim to combine the synthetic tunability and pharmacologic properties typically associated with small molecules with the binding footprints, affinities and specificities of biologics. In this review, we discuss the historical and emerging platforms and approaches to design, screen, select and optimize synthetic "designer" peptidomimetics and synthetic biologics. We examine the inspiration and design of different classes of designer peptidomimetics: (i) macrocyclic peptides, (ii) side chain stabilized peptides, (iii) non-natural peptidomimetics, and (iv) synthetic proteomimetics, and notable examples of their application to challenging biomolecules. Finally, we summarize key learnings and remaining challenges for these molecules to become useful chemical probes and therapeutics for historically undruggable targets.

Cancer pharmacoinformatics: Databases and analytical tools

Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.

Remapping the Chemical Space and the Pharmacological Space of Drugs: What Can We Expect from the Road Ahead?

This work examines the current landscape of drug discovery and development, with a particular focus on the chemical and pharmacological spaces. It emphasizes the importance of understanding these spaces to anticipate future trends in drug discovery. The use of cheminformatics and data analysis enabled in silico exploration of these spaces, allowing a perspective of drugs, approved drugs after 2020, and clinical candidates, which were extracted from the newly released ChEMBL34 (March 2024). This perspective on chemical and pharmacological spaces enables the identification of trends and areas to be occupied, thereby creating opportunities for more effective and targeted drug discovery and development strategies in the future.

Covalent fragment-based drug discovery for target tractability

An important consideration in drug discovery is the prioritization of tractable protein targets that are not only amenable to binding small molecules, but also alter disease biology in response to small molecule binding. Covalent fragment-based drug discovery has emerged as a powerful approach to aid in the identification of such protein targets. The application of irreversible binding mechanisms enables the identification of fragment hits for challenging-to-target proteins, allows proteome-wide screening in a cellular context, and makes it possible to determine functional effects with modestly potent ligands without the requirement for extensive compound optimization. Here, we provide an overview of recent approaches to covalent fragment-based screening and discuss how these have been applied to establish the tractability of unexplored binding sites on protein targets.

Current strategic trends in drug discovery: the present as prologue

INTRODUCTION

Escalating costs and inherent uncertainties associated with drug discovery invite initiatives to improve its efficiency and de-risk campaigns for inventing better therapeutics. One such initiative involves recognizing and exploiting current approaches in therapeutics invention with molecular mechanisms of action that hold promise for designing and targeting new chemical entities as drugs.

AREAS COVERED

This perspective considers the current contextual framework around three drug-discovery approaches and evaluates their potential to help identify new targets/modalities in small-molecule molecular pharmacology: diversifying ligand-directed phenotypes for G protein-coupled receptor (GPCR) pharmacotherapeutic signaling; developing therapeutic-protein degraders and stabilizers for proximity-inducing pharmacology; and mining organelle biology for druggable therapeutic targets.

EXPERT OPINION

The contemporary drug-discovery approaches examined appear generalizable and versatile to have applications in therapeutics invention beyond those case studies discussed herein. Accordingly, they may be considered strategic trends worthy of note in advancing the field toward novel ways of addressing pharmacotherapeutically unmet medical needs.

Strategies for translating proteomics discoveries into drug discovery for dementia

Tauopathies, diseases characterized by neuropathological aggregates of tau including Alzheimer's disease and subtypes of frontotemporal dementia, make up the vast majority of dementia cases. Although there have been recent developments in tauopathy biomarkers and disease-modifying treatments, ongoing progress is required to ensure these are effective, economical, and accessible for the globally ageing population. As such, continued identification of new potential drug targets and biomarkers is critical. "Big data" studies, such as proteomics, can generate information on thousands of possible new targets for dementia diagnostics and therapeutics, but currently remain underutilized due to the lack of a clear process by which targets are selected for future drug development. In this review, we discuss current tauopathy biomarkers and therapeutics, and highlight areas in need of improvement, particularly when addressing the needs of frail, comorbid and cognitively impaired populations. We highlight biomarkers which have been developed from proteomic data, and outline possible future directions in this field. We propose new criteria by which potential targets in proteomics studies can be objectively ranked as favorable for drug development, and demonstrate its application to our group's recent tau interactome dataset as an example.

Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges

The relevant study of transcriptome-wide variations and neurological disorders in the evolved field of genomic data science is on the rise. Deep learning has been highlighted utilizing algorithms on massive amounts of data in a human-like manner, and is expected to predict the dependency or druggability of hidden mutations within the genome. Enormous mutational variants in coding and noncoding transcripts have been discovered along the genome by far, despite of the fine-tuned genetic proofreading machinery. These variants could be capable of inducing various pathological conditions, including neurological disorders, which require lifelong care. Several limitations and questions emerge, including the use of conventional processes via limited patient-driven sequence acquisitions and decoding-based inferences as well as how rare variants can be deduced as a population-specific etiology. These puzzles require harnessing of advanced systems for precise disease prediction, drug development and drug applications. In this review, we summarize the pathophysiological discoveries of pathogenic variants in both coding and noncoding transcripts in neurological disorders, and the current advantage of deep learning applications. In addition, we discuss the challenges encountered and how to outperform them with advancing interpretation.