Refining the impact of genetic evidence on clinical success.

The cost of drug discovery and development is driven primarily by failure1, with only about 10% of clinical programmes eventually receiving approval2-4. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval5. In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency or year of discovery. These results indicate we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.

Responsible use of negative research outcomes-accelerating the discovery and development of new antibiotics.

Failure to share and make use of existing knowledge, particularly negative research outcomes, has been recognized as one of the key sources of waste and inefficiency in the drug discovery and development process. In the field of antibiotic research, providing a platform where negative outcomes could be shared to prevent the vicious cycle of duplicating costly studies that produce the same negative results would greatly de-risk and accelerate the development of new antibiotics. Providing a legally supported framework that recognizes negative outcomes as intellectual contributions, which can subsequently be translated into a revenue-sharing model, may lead to more openness and value creation in support of a sustainable and responsible transformation of research into socially and economically beneficial innovations.

[Consideration on Industry-Academia Partnering in Drug Discovery Development: Based on a Questionnaire Survey of Pharmaceutical Companies and Academia].

Japanese pharmaceutical products continue to experience a trade deficit, since import values exceed export values. In drug discovery development, given the pace of technological innovations, there has been a major shift from low-molecular-weight compounds to biomedicine. It is anticipated that industry, academia and government will work more closely together in support of the pharmaceutical industry. Drug discovery requires much time and vast resources before the results can be put to practical use, and evidence suggests that many newly approved drugs derive from university-sourced technology. Pharmaceutical companies keep a close eye on technology evolving in universities. However, some reports state that there is a substantial difference compared to the development costs of the major Japanese pharmaceutical companies. Therefore, the authors hypothesized that there may be some issues hindering industrial-academic partnerships in drug discovery. In order to understand the actual situation and barriers to promoting industrial-academic collaboration, the Japan Pharmaceutical Manufacturers Association (JPMA), Japan Agency for Medical Research and Development (AMED), and the Medical Industry-Academia Collaboration Network (medU-net) Council will work together in issuing questionnaires and conducting an awareness survey. This survey sought the personal opinions of individuals belonging to JPMA and medU-net. Based on the results of this survey, we will introduce the issues related to industrial-academic collaboration and partnerships, and any gaps between industry and academia. Furthermore, we suggest solutions to promoting drug discovery innovation in Japan.

Automation of absolute protein-ligand binding free energy calculations for docking refinement and compound evaluation.

Absolute binding free energy calculations with explicit solvent molecular simulations can provide estimates of protein-ligand affinities, and thus reduce the time and costs needed to find new drug candidates. However, these calculations can be complex to implement and perform. Here, we introduce the software BAT.py, a Python tool that invokes the AMBER simulation package to automate the calculation of binding free energies for a protein with a series of ligands. The software supports the attach-pull-release (APR) and double decoupling (DD) binding free energy methods, as well as the simultaneous decoupling-recoupling (SDR) method, a variant of double decoupling that avoids numerical artifacts associated with charged ligands. We report encouraging initial test applications of this software both to re-rank docked poses and to estimate overall binding free energies. We also show that it is practical to carry out these calculations cheaply by using graphical processing units in common machines that can be built for this purpose. The combination of automation and low cost positions this procedure to be applied in a relatively high-throughput mode and thus stands to enable new applications in early-stage drug discovery.

Are open-source approaches the most efficient way forward for COVID-19 drug discovery?

Introduction: The COVID-19 pandemic has catalyzed the production of potential antivirals and vaccines from research organizations across the globe. The initial step for all drug discovery models is the identification of suitable targets. One approach organizations may take to tackle this involves issuing raw data publicly for collaboration with other organizations in order to spark discussion, collectively experiment and stay up to date with advances in scientific knowledge. Areas covered: Numerous organizations have released genomic data, amongst other tools, for the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and this has led to the development of growing datasets of knowledge for continued collaboration amongst different scientific communities. A different technique employs a more closed, market-driven method in order to stay ahead financially in the race for developing a suitable antiviral or vaccine. The latter allows sustained motivation for company ambitions and progress has been made toward clinical trials for potential drugs. Expert opinion: A case can be made for both open and closed drug discovery models; however, due to the rapidly evolving nature of this deadly virus, organizations should collate their research and support one another to ensure satisfactory treatment can be approved in a timely manner.

Covalent inhibitor reactivity prediction by the electrophilicity index-in and out of scope.

Drug discovery is an expensive and time-consuming process. To make this process more efficient quantum chemistry methods can be employed. The electrophilicity index is one property that can be calculated by quantum chemistry methods, and if calculated correctly gives insight into the reactivity of covalent inhibitors. Herein we present the usage of the electrophilicity index on three common warheads, i.e., acrylamides, 2-chloroacetamides, and propargylamides. We thoroughly examine the properties of the electrophilicity index, show which pitfalls should be avoided, and what the requirements to successfully apply the electrophilicity index are.

Evolving Outsourcing Landscape in Pharma R&D: Different Collaborative Models and Factors To Consider When Choosing a Contract Research Organization.

Outsourcing has become an integral part of how research and early development (R&D) is executed in biotech companies and large pharmaceutical organizations. Drug discovery organizations can choose from several operational models when partnering with a service provider, ranging from short-term, fee-for-service (FFS)-based arrangements to more strategic full-time-equivalent (FTE)-based collaborations and even risk-sharing relationships. Clients should consider a number of criteria when deciding which contract research organization (CRO) is best positioned to help meet their goals. Besides cost, other factors such as intellectual property protection, problem solving skills, value-creation ability, communication, data integrity, safety and personnel policies, ease of communication, geography, duration of engagement, scalability of capacity, and contractual details deserve proper consideration. In the end, the success of a drug discovery partnership will depend in large part on the people who execute the science.

Computational Chemistry on a Budget: Supporting Drug Discovery with Limited Resources.

An increasing number of new drugs have their origin in small biotech or academia. In contrast to big pharma, these environments are often more limited in terms of resources, and this necessitates different approaches to the drug discovery process. In this review, we outline how computational methods can help advance drug discovery in a setting with more limited resources and we share what, based on our experience, are the best practices for these methods.