Advances in Genomics for Drug Development

How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons

AI techniques are making inroads into the field of drug discovery. As a result, a growing number of drugs and vaccines have been discovered using AI. However, questions remain about the success of these molecules in clinical trials. To address these questions, we conducted a first analysis of the clinical pipelines of AI-native Biotech companies. In Phase I we find AI-discovered molecules have an 80-90% success rate, substantially higher than historic industry averages. This suggests, we argue, that AI is highly capable of designing or identifying molecules with drug-like properties. In Phase II the success rate is ∼40%, albeit on a limited sample size, comparable to historic industry averages. Our findings highlight early signs of the clinical potential of AI-discovered molecules.

Using genetics to investigate the association between lanosterol and cataract

Background: Cataract is one of the most prevalent causes of blindness worldwide. Whilst surgery is the primary treatment for cataracts, it is not always an available option, particularly in developing countries. Non-surgical methods of treatment would increase treatment availability for more patients. Several studies have investigated how topical application of oxysterols, such as lanosterol, may break down aggregated proteins and restore lens transparency. However, the results are conflicting and inconclusive. Aim: In this study, we focus on combining genetic evidence for associations between lanosterol related genetic variation and cataract to explore whether lanosterol is a potentially suitable drug treatment option. Method: Using data from 45,449 available cataract cases from the UK Biobank, with participant ages ranging from 40-69, we conducted a genetic association study (GWAS) to assess the risk of cataract. Cataract cases were defined using diagnostic and operation codes. We focused on genetic variants in the lanosterol synthase gene region. We also compared our results with previously published genetic associations of phytosterol-to-lanosterol ratios. Finally, we performed a genetic risk score analysis to test the association between lanosterol within the cholesterol synthesis pathway and the risk of cataract. Results: No statistically significant single nucleotide polymorphisms (SNPs) associations with cataract were observed in the gene region of lanosterol synthase at a multiple testing adjusted significance threshold of p < 0.05/13. The comparison between cataract risk and genetic association of 8 phytosterol-to-lanosterol GWAS results also showed no evidence to support lanosterol's protective properties for cataract risk. No statistically significant association was found between the lanosterol within the cholesterol synthesis pathway genetic risk score and cataract outcomes (OR = 1.002 p = 0.568). Conclusion: There was no evidence observed for genetic associations between lanosterol and cataract risk. Our results do not support lanosterol's potential role in treating cataracts. Further research may be needed to address the effect of lanosterol on specific cataract subtypes.

Recent Advances in Drug Discovery Toxicology

Major advances in scientific discovery and insights that stem from the development and use of new techniques and models can bring remarkable progress to conventional toxicology. Although animal testing is still considered as the "gold standard" in traditional toxicity testing, there is a necessity for shift from animal testing to alternative methods regarding the drug safety testing owing to the emerging state-of-art techniques and the proposal of 3Rs (replace, reduce, and refine) towards animal welfare. This review describes some recent research methods in drug discovery toxicology, including in vitro cell and organ-on-a-chip, imaging systems, model organisms (C. elegans, Danio rerio, and Drosophila melanogaster), and toxicogenomics in modern toxicology testing.

Drug Repurposing for Spinal Cord Injury: Progress Towards Therapeutic Intervention for Primary Factors and Secondary Complications

Spinal cord injury (SCI) encompasses a plethora of complex mechanisms like the involvement of major cell death pathways, neurodegeneration of spinal cord neurons, overexpression of glutaminergic transmission and inflammation cascade, along with different co-morbidities like neuropathic pain, urinary and sexual dysfunction, respiratory and cardiac failures, making it one of the leading causes of morbidity and mortality globally. Corticosteroids such as methylprednisolone and dexamethasone, and non-steroidal anti-inflammatory drugs such as naproxen, aspirin and ibuprofen are the first-line treatment options for SCI, inhibiting primary and secondary progression by preventing inflammation and action of reactive oxygen species. However, they are constrained by a short effective drug administration window and their pharmacological action being limited to symptomatic relief of the secondary effects related to spinal cord injury only. Although post-injury rehabilitation treatments may enable functional recovery, they take a long time to show results. Drug repurposing might be an innovative method for expanding therapy alternatives, utilising drugs that are already approved by various esteemed federal agencies throughout the world. Reutilising a drug molecule to treat SCI can eliminate the need for expensive and lengthy drug discovery processes and pave the way for new therapeutic approaches in SCI. This review summarises marketed drugs that could be repurposed based on their safety and efficacy data. We also discuss their mechanisms of action and provide a list of repurposed drugs under clinical trials for SCI therapy.

From target discovery to clinical drug development with human genetics

The substantial investments in human genetics and genomics made over the past three decades were anticipated to result in many innovative therapies. Here we investigate the extent to which these expectations have been met, excluding cancer treatments. In our search, we identified 40 germline genetic observations that led directly to new targets and subsequently to novel approved therapies for 36 rare and 4 common conditions. The median time between genetic target discovery and drug approval was 25 years. Most of the genetically driven therapies for rare diseases compensate for disease-causing loss-of-function mutations. The therapies approved for common conditions are all inhibitors designed to pharmacologically mimic the natural, disease-protective effects of rare loss-of-function variants. Large biobank-based genetic studies have the power to identify and validate a large number of new drug targets. Genetics can also assist in the clinical development phase of drugs-for example, by selecting individuals who are most likely to respond to investigational therapies. This approach to drug development requires investments into large, diverse cohorts of deeply phenotyped individuals with appropriate consent for genetically assisted trials. A robust framework that facilitates responsible, sustainable benefit sharing will be required to capture the full potential of human genetics and genomics and bring effective and safe innovative therapies to patients quickly.

Computational approaches for drug repurposing in oncology: untapped opportunity for high value innovation

Historically, the effort by academia and industry to develop new chemical entities into lifesaving drugs has limited success in meeting the demands of today's healthcare. Repurposing drugs that are originally approved by the United States Food and Drug Administration or by regulatory authorities around the globe is an attractive strategy to rapidly develop much-needed therapeutics for oncologic indications that extend from treating cancer to managing treatment-related complications. This review discusses computational approaches to harness existing drugs for new therapeutic use in oncology.

How to Design Peptides

Novel design of proteins to target receptors for treatment or tissue augmentation has come to the fore owing to advancements in computing power, modeling frameworks, and translational successes. Shorter proteins, or peptides, can offer combinatorial synergies with dendrimer, polymer, or other peptide carriers for enhanced local signaling, which larger proteins may sterically hinder. Here, we present a generalized method for designing a novel peptide. We first show how to create a script protocol that can be used to iteratively optimize and screen novel peptide sequences for binding a target protein. We present a step-by-step introduction to utilizing file repositories, data bases, and the Rosetta software suite. RosettaScripts, an .xml interface that allows for sequential functions to be performed, is used to order the functions for repeatable performance. These strategies may lead to more groups venturing into computational design, which may result in synergies from artificial intelligence/machine learning (AI/ML) to phage display and screening. Importantly, the beginner is expected to be able to design their first peptide ligand and begin their journey in peptide drug discovery. Generally, these peptides potentially could be used to interact with any enzyme or receptor, for example, in the study of chemokines and their interactions with glycosoaminoglycans and their receptors.

PROTAC degraders as chemical probes for studying target biology and target validation

Small molecule degraders such as PROTACs (PROteolysis TArgeting Chimeras) have emerged as new promising pharmacological modalities and the first PROTAC drug candidates are now studied clinically. The catalytic properties of PROTACs, acting as chemical degraders of a protein of interest (POI), represent an attractive new strategy for drug development. The development and characterization of PROTACs requires an array of additional assay systems that track the degradation pathway leading ultimately to degradation of the POI, identifying critical steps for PROTAC optimization. In addition to their exciting translational potential, PROTACs represent versatile chemical tools that considerably expanded our chemical biology toolbox and significantly enlarged the proteome that can be modulated by small molecules. Similar to conventional chemical probes, PROTACs used as chemical probes in target validation require comprehensive characterization. As a consequence, PROTAC-specific quality criteria should be defined by the chemical biology community. These criteria need to comprise additional or alternative parameters compared to those for conventional occupancy-driven chemical probes, such as the maximum level of target degradation (D_max), confirmation of a proteasome dependent degradation mechanism and, importantly, also kinetic parameters of POI degradation. The kinetic aspects are particularly relevant for PROTACs that harbor covalent binding moieties. Here, we review recent progress in the development of assay systems for PROTAC characterization and suggest a set of criteria for PROTACs as high quality chemical probes.

Identification of Therapeutic Targets in an Emerging Gastrointestinal Pathogen Campylobacter ureolyticus and Possible Intervention through Natural Products

Campylobacter ureolyticus is a Gram-negative, anaerobic, non-spore-forming bacteria that causes gastrointestinal infections. Being the most prevalent cause of bacterial enteritis globally, infection by this bacterium is linked with significant morbidity and mortality in children and immunocompromised patients. No information on pan-therapeutic drug targets for this species is available yet. In the current study, a pan-genome analysis was performed on 13 strains of C. ureolyticus to prioritize potent drug targets from the identified core genome. In total, 26 druggable proteins were identified using subtractive genomics. To the best of the authors’ knowledge, this is the first report on the mining of drug targets in C. ureolyticus. UDP-3-O-acyl-N-acetylglucosamine deacetylase (LpxC) was selected as a promiscuous pharmacological target for virtual screening of two bacterial-derived natural product libraries, i.e., postbiotics (n = 78) and streptomycin (n = 737) compounds. LpxC inhibitors from the ZINC database (n = 142 compounds) were also studied with reference to LpxC of C. ureolyticus. The top three docked compounds from each library (including ZINC26844580, ZINC13474902, ZINC13474878, Notoginsenoside St-4, Asiaticoside F, Paraherquamide E, Phytoene, Lycopene, and Sparsomycin) were selected based on their binding energies and validated using molecular dynamics simulations. To help identify potential risks associated with the selected compounds, ADMET profiling was also performed and most of the compounds were considered safe. Our findings may serve as baseline information for laboratory studies leading to the discovery of drugs for use against C. ureolyticus infections.

Global profiling of phosphorylation-dependent changes in cysteine reactivity

Proteomics has revealed that the ~20,000 human genes engender a far greater number of proteins, or proteoforms, that are diversified in large part by post-translational modifications (PTMs). How such PTMs affect protein structure and function is an active area of research but remains technically challenging to assess on a proteome-wide scale. Here, we describe a chemical proteomic method to quantitatively relate serine/threonine phosphorylation to changes in the reactivity of cysteine residues, a parameter that can affect the potential for cysteines to be post-translationally modified or engaged by covalent drugs. Leveraging the extensive high-stoichiometry phosphorylation occurring in mitotic cells, we discover numerous cysteines that exhibit phosphorylation-dependent changes in reactivity on diverse proteins enriched in cell cycle regulatory pathways. The discovery of bidirectional changes in cysteine reactivity often occurring in proximity to serine/threonine phosphorylation events points to the broad impact of phosphorylation on the chemical reactivity of proteins and the future potential to create small-molecule probes that differentially target proteoforms with PTMs.

Clinical Utility of Genomic Testing in Cancer Care

In the era of personalized medicine, physicians rely on their understanding of clinical utility to assess the value of rapidly evolving genetic and genomic tests. Current definitions of the clinical utility of genetic testing sufficiently capture a range of benefits and risks that derive from positive and negative results of tests that assess one gene or a few genes. However, these definitions of clinical utility are inadequate to recognize the wider scope of benefits that accrue from more comprehensive genomic tests, which can develop data sets that inform clinical decision making as well as population health and scientific advancement in novel ways.

Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy

Rapid methodological advances in statistical and computational genomics have enabled researchers to better identify and interpret both rare and common variants responsible for complex human diseases. As we continue to see an expansion of these advances in the field, it is now imperative for researchers to understand the resources and methodologies available for various data types and study designs. In this review, we provide an overview of recent methods for identifying rare and common variants and understanding their roles in disease etiology. Additionally, we discuss the strategy, challenge, and promise of gene therapy. As computational and statistical approaches continue to improve, we will have an opportunity to translate human genetic findings into personalized health care.

Generating Custom Pooled CRISPR Libraries for Genetic Dissection of Biological Pathways

Genetic screens are a classic approach to dissecting biological pathways including membrane trafficking. Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 have enabled the utility of this approach in diploid models, including cultured mammalian cells. Here, we present detailed protocols for generating custom CRISPR libraries. These methods are useful for generating genome-wide libraries for new model organisms that lack an existing genome-wide library, and for generating smaller focused libraries.

Drug-Drug Interactions in Vestibular Diseases, Clinical Problems, and Medico-Legal Implications

Peripheral vestibular disease can be treated with several approaches (e.g., maneuvers, surgery, or medical approach). Comorbidity is common in elderly patients, so polytherapy is used, but it can generate the development of drug-drug interactions (DDIs) that play a role in both adverse drug reactions and reduced adherence. For this reason, they need a complex kind of approach, considering all their individual characteristics. Physicians must be able to prescribe and deprescribe drugs based on a solid knowledge of pharmacokinetics, pharmacodynamics, and clinical indications. Moreover, full information is required to reach a real therapeutic alliance, to improve the safety of care and reduce possible malpractice claims related to drug-drug interactions. In this review, using PubMed, Embase, and Cochrane library, we searched articles published until 30 August 2021, and described both pharmacokinetic and pharmacodynamic DDIs in patients with vestibular disorders, focusing the interest on their clinical implications and on risk management strategies.