Improving the odds of drug development success through human genomics: modelling study

The Emerging Role of Neuronal Organoid Models in Drug Discovery: Potential Applications and Hurdles to Implementation

The high failure rate of drugs in the clinical pipeline is likely in part the result of inadequate preclinical models, particularly those for neurologic disorders and neurodegenerative disease. Such preclinical animal models often suffer from fundamental species differences and rarely recapitulate all facets of neurologic conditions, whereas conventional two-dimensional (2D) in vitro models fail to capture the three-dimensional spatial organization and cell-to-cell interactions of brain tissue that are presumed to be critical to the function of the central nervous system. Recent studies have suggested that stem cell-derived neuronal organoids are more physiologically relevant than 2D neuronal cultures because of their cytoarchitecture, electrophysiological properties, human origin, and gene expression. Hence there is interest in incorporating such physiologically relevant models into compound screening and lead optimization efforts within drug discovery. However, despite their perceived relevance, compared with previously used preclinical models, little is known regarding their predictive value. In fact, some have been wary to broadly adopt organoid technology for drug discovery because of the low-throughput and tedious generation protocols, inherent variability, and lack of compatible moderate-to-high-throughput screening assays. Consequently, microfluidic platforms, specialized bioreactors, and automated assays have been and are being developed to address these deficits. This mini review provides an overview of the gaps to broader implementation of neuronal organoids in a drug discovery setting as well as emerging technologies that may better enable their utilization. SIGNIFICANCE STATEMENT: Neuronal organoid models offer the potential for a more physiological system in which to study neurological diseases, and efforts are being made to employ them not only in mechanistic studies but also in profiling/screening purposes within drug discovery. In addition to exploring the utility of neuronal organoid models within this context, efforts in the field aim to standardize such models for consistency and adaptation to screening platforms for throughput evaluation. This review covers potential impact of and hurdles to implementation.

Rethinking animal models of sepsis - working towards improved clinical translation whilst integrating the 3Rs

Sepsis is a major worldwide healthcare issue with unmet clinical need. Despite extensive animal research in this area, successful clinical translation has been largely unsuccessful.

We propose one reason for this is that, sometimes, the experimental question is misdirected or unrealistic expectations are being made of the animal model.

As sepsis models can lead to a rapid and substantial suffering – it is essential that we continually review experimental approaches and undertake a full harm:benefit impact assessment for each study. In some instances, this may require refinement of existing sepsis models. In other cases, it may be replacement to a different experimental system altogether, answering a mechanistic question whilst aligning with the principles of reduction, refinement and replacement (3Rs).

We discuss making better use of patient data to identify potentially useful therapeutic targets which can subsequently be validated in preclinical systems. This may be achieved through greater use of construct validity models, from which mechanistic conclusions are drawn. We argue that such models could provide equally useful scientific data as face validity models, but with an improved 3Rs impact. Indeed, construct validity models may not require sepsis to be modelled, per se. We propose that approaches that could support and refine clinical translation of research findings, whilst reducing the overall welfare burden on research animals.

Mendelian randomization for studying the effects of perturbing drug targets

Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline.

Use of artificial intelligence to enhance phenotypic drug discovery

Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.

Toward Developing Immunocompetent Diabetic Foot Ulcer-on-a-Chip Models for Drug Testing

Bioengineering of skin has been significantly explored, ranging from the use of traditional cell culture systems to the most recent organ-on-a-chip (OoC) technology that permits skin modeling on physiological scales among other benefits. This article presents key considerations for developing physiologically relevant immunocompetent diabetic foot ulcer (DFU) models. Diabetic foot ulceration affects hundreds of millions of individuals globally, especially the elderly, and constitutes a major socioeconomic burden. When DFUs are not treated and managed in a timely manner, 15-50% of patients tend to undergo partial or complete amputation of the affected limb. Consequently, at least 40% of such patients die within 5 years postamputation. Currently, therapeutic strategies are actively sought and developed. However, present-day preclinical platforms (animals and in vitro models) are not robust enough to provide reliable data for clinical trials. Insights from published works on immunocompetent skin-on-a-chip models and bioengineering considerations, presented in this article, can inform researchers on how to develop robust OoC models for testing topical therapies such as growth factor-based therapies for DFUs. We propose that immunocompetent DFU-on-a-chip models should be bioengineered using diseased cells derived from individuals; in particular, the pathophysiological contribution of macrophages in diabetic wound healing, along with the typical fibroblasts and keratinocytes, needs to be recapitulated. The ideal model should consist of the following components: diseased cells embedded in reproducible scaffolds, which permit endogenous "diseased" extracellular matrix deposition, and the integration of the derived immunocompetent DFU model onto a microfluidic platform. The proposed DFU platforms will eventually facilitate reliable and robust drug testing of wound healing therapeutics, coupled with reduced clinical trial failure rates. Impact statement Current animal and cell-based systems are not physiologically relevant enough to retrieve reliable results for clinical translation of diabetic foot ulcer (DFU) therapies. Organ-on-a-chip (OoC) technology offers desirable features that could finally enable the vision of modeling DFU for pathophysiological studies and drug testing at a microscale. This article brings together the significant recent findings relevant to developing a minimally functional immunocompetent DFU-on-a-chip model, as wound healing cannot occur without a proper functioning immune response. It looks feasible in the future to recapitulate the stagnant inflammation in DFU (thought to impede wound healing) using OoC, diseased cells, and an endogenously produced extracellular matrix.

Host Directed Therapies for Tuberculous Meningitis

A dysregulated host immune response significantly contributes to morbidity and mortality in tuberculous meningitis (TBM). Effective host directed therapies (HDTs) are critical to improve survival and clinical outcomes. Currently only one HDT, dexamethasone, is proven to improve mortality. However, there is no evidence dexamethasone reduces morbidity, how it reduces mortality is uncertain, and it has no proven benefit in HIV co-infected individuals. Further research on these aspects of its use, as well as alternative HDTs such as aspirin, thalidomide and other immunomodulatory drugs is needed. Based on new knowledge from pathogenesis studies, repurposed therapeutics which act upon small molecule drug targets may also have a role in TBM. Here we review existing literature investigating HDTs in TBM, and propose new rationale for the use of novel and repurposed drugs. We also discuss host variable responses and evidence to support a personalised approach to HDTs in TBM.

Deep inverse reinforcement learning for structural evolution of small molecules

The size and quality of chemical libraries to the drug discovery pipeline are crucial for developing new drugs or repurposing existing drugs. Existing techniques such as combinatorial organic synthesis and high-throughput screening usually make the process extraordinarily tough and complicated since the search space of synthetically feasible drugs is exorbitantly huge. While reinforcement learning has been mostly exploited in the literature for generating novel compounds, the requirement of designing a reward function that succinctly represents the learning objective could prove daunting in certain complex domains. Generative adversarial network-based methods also mostly discard the discriminator after training and could be hard to train. In this study, we propose a framework for training a compound generator and learn a transferable reward function based on the entropy maximization inverse reinforcement learning (IRL) paradigm. We show from our experiments that the IRL route offers a rational alternative for generating chemical compounds in domains where reward function engineering may be less appealing or impossible while data exhibiting the desired objective is readily available.

Applications of Machine Learning in Drug Target Discovery

Drug target discovery is a critical step in drug development. It is the basis of modern drug development because it determines the target molecules related to specific diseases in advance. Predicting drug targets by computational methods saves a great deal of financial and material resources compared to in vitro experiments. Therefore, several computational methods for drug target discovery have been designed. Recently, machine learning (ML) methods in biomedicine have developed rapidly. In this paper, we present an overview of drug target discovery methods based on machine learning. Considering that some machine learning methods integrate network analysis to predict drug targets, network-based methods are also introduced in this article. Finally, the challenges and future outlook of drug target discovery are discussed.

DeepH-DTA: Deep Learning for Predicting Drug-Target Interactions: A Case Study of COVID-19 Drug Repurposing

The rapid spread of novel coronavirus pneumonia (COVID-19) has led to a dramatically increased mortality rate worldwide. Despite many efforts, the rapid development of an effective vaccine for this novel virus will take considerable time and relies on the identification of drug-target (DT) interactions utilizing commercially available medication to identify potential inhibitors. Motivated by this, we propose a new framework, called DeepH-DTA, for predicting DT binding affinities for heterogeneous drugs. We propose a heterogeneous graph attention (HGAT) model to learn topological information of compound molecules and bidirectional ConvLSTM layers for modeling spatio-sequential information in simplified molecular-input line-entry system (SMILES) sequences of drug data. For protein sequences, we propose a squeezed-excited dense convolutional network for learning hidden representations within amino acid sequences; while utilizing advanced embedding techniques for encoding both kinds of input sequences. The performance of DeepH-DTA is evaluated through extensive experiments against cutting-edge approaches utilising two public datasets (Davis, and KIBA) which comprise eclectic samples of the kinase protein family and the pertinent inhibitors. DeepH-DTA attains the highest Concordance Index (CI) of 0.924 and 0.927 and also achieved a mean square error (MSE) of 0.195 and 0.111 on the Davis and KIBA datasets respectively. Moreover, a study using FDA-approved drugs from the Drug Bank database is performed using DeepH-DTA to predict the affinity scores of drugs against SARS-CoV-2 amino acid sequences, and the results show that that the model can predict some of the SARS-Cov-2 inhibitors that have been recently approved in many clinical studies.

A guide for bioinformaticians: 'omics-based drug discovery for precision oncology

Bioinformatics-centric drug development is inevitable in the era of precision medicine. Clinical 'omics information, including genomics, epigenomics, transcriptomics, and proteomics, provides the most comprehensive molecular landscape in which each patient's pathological history is delineated. Hence, the capability of bioinformaticians to manage integrative 'omics data is crucial to current drug development. Bioinformatics can accelerate drug development from initial time-consuming discoveries to the clinical stage by providing information-guided solutions. However, many bioinformaticians do not have opportunities to participate in drug discovery programs. As a starting point for bioinformaticians with no prior drug development experience, here we discuss bioinformatics applications during drug development with a focus on working-level omics-based methodologies.

Druggability and drug-likeness concepts in drug design: are biomodelling and predictive tools having their say?

The drug discovery process typically involves target identification and design of suitable drug molecules against these targets. Despite decades of experimental investigations in the drug discovery domain, about 96% overall failure rate has been recorded in drug development due to the "undruggability" of various identified disease targets, in addition to other challenges. Likewise, the high attrition rate of drug candidates in the drug discovery process has also become an enormous challenge for the pharmaceutical industry. To alleviate this negative outlook, new trends in drug discovery have emerged. By drifting away from experimental research methods, computational tools and big data are becoming valuable in the prediction of biological target druggability and the drug-likeness of potential therapeutic agents. These tools have proven to be useful in saving time and reducing research costs. As with any emerging technique, however, controversial opinions have been presented regarding the validation of predictive computational tools. To address the challenges associated with these varying opinions, this review attempts to highlight the principles of druggability and drug-likeness and their recent advancements in the drug discovery field. Herein, we present the different computational tools and their reliability of predictive analysis in the drug discovery domain. We believe that this report would serve as a comprehensive guide towards computational-oriented drug discovery research. Graphical abstract Highlights of methods for assessing the druggability of biological targets.

Lipid lowering and Alzheimer disease risk: A mendelian randomization study

OBJECTIVE

To examine whether genetic variation affecting the expression or function of lipid-lowering drug targets is associated with Alzheimer disease (AD) risk, to evaluate the potential impact of long-term exposure to corresponding therapeutics.

METHODS

We conducted Mendelian randomization analyses using variants in genes that encode the protein targets of several approved lipid-lowering drug classes: HMGCR (encoding the target for statins), PCSK9 (encoding the target for PCSK9 inhibitors, eg, evolocumab and alirocumab), NPC1L1 (encoding the target for ezetimibe), and APOB (encoding the target of mipomersen). Variants were weighted by associations with low-density lipoprotein cholesterol (LDL-C) using data from lipid genetics consortia (n up to 295,826). We meta-analyzed Mendelian randomization estimates for regional variants weighted by LDL-C on AD risk from 2 large samples (total n = 24,718 cases, 56,685 controls).

RESULTS

Models for HMGCR, APOB, and NPC1L1 did not suggest that the use of related lipid-lowering drug classes would affect AD risk. In contrast, genetically instrumented exposure to PCSK9 inhibitors was predicted to increase AD risk in both of the AD samples (combined odds ratio per standard deviation lower LDL-C inducible by the drug target = 1.45, 95% confidence interval = 1.23-1.69). This risk increase was opposite to, although more modest than, the degree of protection from coronary artery disease predicted by these same methods for PCSK9 inhibition.

INTERPRETATION

We did not identify genetic support for the repurposing of statins, ezetimibe, or mipomersen for AD prevention. Notwithstanding caveats to this genetic evidence, pharmacovigilance for AD risk among users of PCSK9 inhibitors may be warranted. ANN NEUROL 2020;87:30-39.