Human genetics as a model for target validation: finding new therapies for diabetes

Improving target assessment in biomedical research: the GOT-IT recommendations

Academic research plays a key role in identifying new drug targets, including understanding target biology and links between targets and disease states. To lead to new drugs, however, research must progress from purely academic exploration to the initiation of efforts to identify and test a drug candidate in clinical trials, which are typically conducted by the biopharma industry. This transition can be facilitated by a timely focus on target assessment aspects such as target-related safety issues, druggability and assayability, as well as the potential for target modulation to achieve differentiation from established therapies. Here, we present recommendations from the GOT-IT working group, which have been designed to support academic scientists and funders of translational research in identifying and prioritizing target assessment activities and in defining a critical path to reach scientific goals as well as goals related to licensing, partnering with industry or initiating clinical development programmes. Based on sets of guiding questions for different areas of target assessment, the GOT-IT framework is intended to stimulate academic scientists' awareness of factors that make translational research more robust and efficient, and to facilitate academia-industry collaboration.

The endless frontier? The recent increase of R&D productivity in pharmaceuticals

BACKGROUND

Studies on the early 2000s documented increasing attrition rates and duration of clinical trials, leading to a representation of a "productivity crisis" in pharmaceutical research and development (R&D). In this paper, we produce a new set of analyses for the last decade and report a recent increase of R&D productivity within the industry.

METHODS

We use an extensive data set on the development history of more than 50,000 projects between 1990 and 2017, which we integrate with data on sales, patents, and anagraphical information on each institution involved. We devise an indicator to quantify the novelty of each project, based on its set of mechanisms of action.

RESULTS

First, we investigate how R&D projects are allocated across therapeutic areas and find a polarization towards high uncertainty/high potential reward indications, with a strong focus on oncology. Second, we find that attrition rates have been decreasing at all stages of clinical research in recent years. In parallel, for each phase, we observe a significant reduction of time required to identify projects to be discontinued. Moreover, our analysis shows that more recent successful R&D projects are increasingly based on novel mechanisms of action and target novel indications, which are characterized by relatively small patient populations. Third, we find that the number of R&D projects on advanced therapies is also growing. Finally, we investigate the relative contribution to productivity variations of different types of institutions along the drug development process, with a specific focus on the distinction between the roles of Originators and Developers of R&D projects. We document that in the last decade Originator-Developer collaborations in which biotech companies act as Developers have been growing in importance. Moreover, we show that biotechnology companies have reached levels of productivity in project development that are equivalent to those of large pharmaceutical companies.

CONCLUSIONS

Our study reports on the state of R&D productivity in the bio-pharmaceutical industry, finding several signals of an improving performance, with R&D projects becoming more targeted and novel in terms of indications and mechanisms of action.

A Comprehensive Review on Preclinical Diabetic Models

BACKGROUND

Preclinical experimental models historically play a critical role in the exploration and characterization of disease pathophysiology. Further, these in-vivo and in-vitro preclinical experiments help in target identification, evaluation of novel therapeutic agents and validation of treatments.

INTRODUCTION

Diabetes mellitus (DM) is a multifaceted metabolic disorder of multidimensional aetiologies with the cardinal feature of chronic hyperglycemia. To avoid or minimize late complications of diabetes and related costs, primary prevention and early treatment are therefore necessary. Due to its chronic manifestations, new treatment strategies need to be developed, because of the limited effectiveness of the current therapies.

METHODS

The study included electronic databases such as Pubmed, Web of Science and Scopus. The datasets were searched for entries of studies up to June, 2018.

RESULTS

A large number of in-vivo and in-vitro models have been presented for evaluating the mechanism of anti-hyperglycaemic effect of drugs in hormone-, chemically-, pathogen-induced animal models of diabetes mellitus. The advantages and limitations of each model have also been addressed in this review.

CONCLUSION

This review encompasses the wide pathophysiological and molecular mechanisms associated with diabetes, particularly focusing on the challenges associated with the evaluation and predictive validation of these models as ideal animal models for preclinical assessments and discovering new drugs and therapeutic agents for translational application in humans. This review may further contribute to discover a novel drug to treat diabetes more efficaciously with minimum or no side effects. Furthermore, it also highlights ongoing research and considers the future perspectives in the field of diabetes.

Role of Tmem163 in zinc-regulated insulin storage of MIN6 cells: Functional exploration of an Indian type 2 diabetes GWAS associated gene

Genome wide association study for type 2 diabetes discovered TMEM163 as a risk locus. Perturbations in TMEM163 expression was reported to be associated with impaired intracellular zinc homeostasis. Physiological concentration of zinc is instrumental to maintain insulin storage and functionality in pancreatic β cells. We found abundant TMEM163 expression in human pancreas, both at transcriptional and translational levels. Knockdown of endogenous Tmem163 in MIN6 cells resulted in increased intracellular zinc and total insulin content, coupled with compromised insulin secretion at high glucose stimuli. Furthermore, Tmem163 knockdown led to enhanced cellular glucose uptake. Upon next generation sequencing, one-third of the studied T2D patients were found to have a novel missense variant in TMEM163 gene. Study participants harboring this missense variant displayed a trend of higher glycemic indices. This is the first report on exploring the biological role of TMEM163 in relation to T2D pathophysiology.

Human Genetics of Obesity and Type 2 Diabetes Mellitus: Past, Present, and Future

Type 2 diabetes mellitus (T2D) and obesity already represent 2 of the most prominent risk factors for cardiovascular disease, and are destined to increase in importance given the global changes in lifestyle. Ten years have passed since the first round of genome-wide association studies for T2D and obesity. During this decade, we have witnessed remarkable developments in human genetics. We have graduated from the despair of candidate gene-based studies that generated few consistently replicated genotype-phenotype associations, to the excitement of an exponential harvest of loci robustly associated with medical outcomes through ever larger genome-wide association study meta-analyses. As well as discovering hundreds of loci, genome-wide association studies have provided transformative insights into the genetic architecture of T2D and other complex traits, highlighting the extent of polygenicity and the tiny effect sizes of many common risk alleles. Genome-wide association studies have also provided a critical starting point for discovering new biology relevant to these traits. Expectations are high that these discoveries will foster development of more effective strategies for intervention, through optimization of precision medicine approaches. In this article, we review current knowledge and provide suggestions for the next steps in genetic research for T2D and obesity. We focus on four areas relevant to precision medicine: genetic architecture, pharmacogenetics and other gene-environment interactions, mechanistic inference, and drug development. As we describe, the genetic architecture of complex traits has major implications for the prospects of precision medicine, rendering some anticipated approaches decidedly unrealistic. We highlight obstacles to the translation of human genetic findings into mechanism inference but are optimistic that, as these are overcome, there is untapped potential for novel drugs and more effective strategies for treating and preventing T2D and obesity.

Genomic annotation of disease-associated variants reveals shared functional contexts

Variation in non-coding DNA, encompassing gene regulatory regions such as enhancers and promoters, contributes to risk for complex disorders, including type 2 diabetes. While genome-wide association studies have successfully identified hundreds of type 2 diabetes loci throughout the genome, the vast majority of these reside in non-coding DNA, which complicates the process of determining their functional significance and level of priority for further study. Here we review the methods used to experimentally annotate these non-coding variants, to nominate causal variants and to link them to diabetes pathophysiology. In recent years, chromatin profiling, massively parallel sequencing, high-throughput reporter assays and CRISPR gene editing technologies have rapidly become indispensable tools. Rather than treating individual variants in isolation, we discuss the importance of accounting for context, both genetic (such as flanking DNA sequence) and environmental (such as cellular state or environmental exposure). Incorporating these features shows promise in terms of revealing biologically convergent molecular signatures across distant and seemingly unrelated loci. Studying regulatory elements in the proper context will be crucial for interpreting the functional significance of disease-associated variants and applying the resulting knowledge to improve patient care.

A scientometric review of genome-wide association studies

This scientometric review of genome-wide association studies (GWAS) from 2005 to 2018 (3639 studies; 3508 traits) reveals extraordinary increases in sample sizes, rates of discovery and traits studied. A longitudinal examination shows fluctuating ancestral diversity, still predominantly European Ancestry (88% in 2017) with 72% of discoveries from participants recruited from three countries (US, UK, Iceland). US agencies, primarily NIH, fund 85% and women are less often senior authors. We generate a unique GWAS H-Index and reveal a tight social network of prominent authors and frequently used data sets. We conclude with 10 evidence-based policy recommendations for scientists, research bodies, funders, and editors.

Capitalizing on Insights from Human Genetics to Identify Novel Therapeutic Targets for Coronary Artery Disease

Coronary artery disease (CAD) is a major cause of morbidity and mortality. Unfortunately, despite decades of research focused on disease pathogenesis, we still lack a sufficient pharmacopeia for preventing CAD. The failure of many novel cardiovascular drugs to improve clinical outcomes reflects the major substantial challenge of drug development: identifying causal mechanisms that can be therapeutically manipulated to lower disease risk. Identifying genetic variants that are associated with risk of CAD has emerged as a clear path toward improving our understanding of the underlying mechanisms that lead to disease and to the development of new therapies. Here, we review the potential utility and limitations of using human genetics to guide the identification of therapeutic targets for CAD.

Precision medicine in diabetes prevention, classification and management

Diabetes has become a major burden of healthcare expenditure. Diabetes management following a uniform treatment algorithm is often associated with progressive treatment failure and development of diabetic complications. Recent advances in our understanding of the genomic architecture of diabetes and its complications have provided the framework for development of precision medicine to personalize diabetes prevention and management. In the present review, we summarized recent advances in the understanding of the genetic basis of diabetes and its complications. From a clinician's perspective, we attempted to provide a balanced perspective on the utility of genomic medicine in the field of diabetes. Using genetic information to guide management of monogenic forms of diabetes represents the best-known examples of genomic medicine for diabetes. Although major strides have been made in genetic research for diabetes, its complications and pharmacogenetics, ongoing efforts are required to translate these findings into practice by incorporating genetic information into a risk prediction model for prioritization of treatment strategies, as well as using multi-omic analyses to discover novel drug targets with companion diagnostics. Further research is also required to ensure the appropriate use of this information to empower individuals and healthcare professionals to make personalized decisions for achieving the optimal outcome.

Type 2 diabetes risk alleles in PAM impact insulin release from human pancreatic β-cells

The molecular mechanisms underpinning susceptibility loci for type 2 diabetes (T2D) remain poorly understood. Coding variants in peptidylglycine α-amidating monooxygenase (PAM) are associated with both T2D risk and insulinogenic index. Here, we demonstrate that the T2D risk alleles impact negatively on overall PAM activity via defects in expression and catalytic function. PAM deficiency results in reduced insulin content and altered dynamics of insulin secretion in a human β-cell model and primary islets from cadaveric donors. Thus, our results demonstrate a role for PAM in β-cell function, and establish molecular mechanisms for T2D risk alleles at this locus.

Big Data in Drug Discovery

Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.

Pharmacogenetics: An Important Part of Drug Development with A Focus on Its Application

Since the human genome project in 2003, the view of personalized medicine to improve diagnosis and cure diseases at the molecular level became more real. Sequencing the human genome brought some benefits in medicine such as early detection of diseases with a genetic predisposition, treating patients with rare diseases, the design of gene therapy and the understanding of pharmacogenetics in the metabolism of drugs. This review explains the concepts of pharmacogenetics, polymorphisms, mutations, variations, and alleles, and how this information has helped us better understand the metabolism of drugs. Multiple resources are presented to promote reducing the gap between scientists, physicians, and patients in understanding the use and benefits of pharmacogenetics. Some of the most common clinical examples of genetic variants and how pharmacogenetics was used to determine treatment options for patients having these variants were discussed. Finally, we evaluated some of the challenges of implementing pharmacogenetics in a clinical setting and proposed actions to be taken to make pharmacogenetics a standard diagnostic tool in personalized medicine.

Prioritising Causal Genes at Type 2 Diabetes Risk Loci

PURPOSE OF REVIEW

Genome-wide association studies (GWAS) for type 2 diabetes (T2D) risk have identified a large number of genetic loci associated with disease susceptibility. However, progress moving from association signals through causal genes to functional understanding has so far been slow, hindering clinical translation. This review discusses the benefits and limitations of emerging, unbiased approaches for prioritising causal genes at T2D risk loci.

RECENT FINDINGS

Candidate causal genes can be identified by a number of different strategies that rely on genetic data, genomic annotations, and functional screening of selected genes. To overcome the limitations of each particular method, integration of multiple data sets is proving essential for establishing confidence in the prioritised genes. Previous studies have also highlighted the need to support these efforts through identification of causal variants and disease-relevant tissues. Prioritisation of causal genes at T2D risk loci by integrating complementary lines of evidence promises to accelerate our understanding of disease pathology and promote translation into new therapeutics.