Drug discovery in academia

DruMAP: A Novel Drug Metabolism and Pharmacokinetics Analysis Platform

We developed a novel drug metabolism and pharmacokinetics (DMPK) analysis platform named DruMAP. This platform consists of a database for DMPK parameters and programs that can predict many DMPK parameters based on the chemical structure of a compound. The DruMAP database includes curated DMPK parameters from public sources and in-house experimental data obtained under standardized conditions; it also stores predicted DMPK parameters produced by our prediction programs. Users can predict several DMPK parameters simultaneously for novel compounds not found in the database. Furthermore, the highly flexible search system enables users to search for compounds as they desire. The current version of DruMAP comprises more than 30,000 chemical compounds, about 40,000 activity values (collected from public databases and in-house data), and about 600,000 predicted values. Our platform provides a simple tool for searching and predicting DMPK parameters and is expected to contribute to the acceleration of new drug development. DruMAP can be freely accessed at: https://drumap.nibiohn.go.jp/.

Analysing supply chain coordination mechanisms dealing with repurposing challenges during Covid-19 pandemic in an emerging economy: a multi-layer decision making approach

Following the outbreak of the Covid-19 pandemic, there was a serious need for the pharmaceutical industry to combat the disease more quickly and effectively. In this regard, numerous companies set out to repurpose current drugs. The noticed decision has major challenges in various dimensions, including the creation and management of an efficient supply chain. The present study attempts to examine the significance and relationships of the repurposing challenges and analyze the effectiveness of supply chain coordination contracts confronting them. In this regard, a combination of Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) named DANP method is applied to investigate the relationships and extracting the weights of the mentioned challenges and the multi-criteria optimization and compromise solution technique called VIKOR is employed to prioritize the supply chain coordination contracts found on their impact facing with repurposing challenges. The mentioned techniques have been conducted under the condition of linguistic Z-numbers. The results demonstrated that financial support and digitalization are the most influential challenges. Moreover, collaboration and data availability have the most weight. In addition, four contracts including effort sharing, cost-sharing, credit option and buyback are the best contracts that companies in the merging economy of Iran should concentrate on them. This research proposes a novel framework of decision-making by integrating DANP and VIKOR with linguistic Z-numbers. Additionally, this study takes a new look at the use of coordination contracts from the viewpoint of repurposing challenges which is highlighted particularly during the Covid-19 pandemic.

In Silico Prediction of Fraction Unbound in Human Plasma from Chemical Fingerprint Using Automated Machine Learning

Predicting the fraction unbound of a drug in plasma plays a significant role in understanding its pharmacokinetic properties during in vitro studies of drug design and discovery. Owing to the gaining reliability of machine learning in biological predictive models and development of automated machine learning techniques for the ease of nonexperts of machine learning to optimize and maximize the reliability of the model, in this experiment, we built an in silico prediction model of a fraction unbound drug in human plasma using a chemical fingerprint and a freely available AutoML framework. The predictive model was trained on one of the largest data sets ever of 5471 experimental values using four different AutoML frameworks to compare their performance on this problem and to choose the most significant one. With a coefficient of determination of 0.85 on the test data set, our best prediction model showed better performance than other previously published models, giving our model significant importance in pharmacokinetic modeling.

Academic Discovery of Anticancer Drugs: Historic and Future Perspectives

The identification and prosecution of meritorious anticancer drug targets and the discovery of clinical candidates represent an extraordinarily time- and resource-intensive process, and the current failure rate of late-stage drugs is a critical issue that must be addressed. Relationships between academia and industry in drug discovery and development have continued to change over time as a result of technical and financial challenges and, most importantly, to the objective of translating impactful scientific discoveries into clinical opportunities. This Golden Age of anticancer drug discovery features an increased appreciation for the high-risk, high-innovation research conducted in the nonprofit sector, with the goals of infusing commercial drug development with intellectual capital and curating portfolios that are financially tenable and clinically meaningful. In this review, we discuss the history of academic-industry interactions in the context of antidrug discovery and offer a view of where these interactions are likely headed as we continue to reach new horizons in our understanding of the immense complexities of cancer biology.

The transition to magic bullets - transition state analogue drug design

In the absence of industry partnerships, most academic groups lack the infrastructure to rationally design and build drugs via methods used in industry. Instead, academia needs to work smarter using mechanism-based design. Working smarter can mean the development of new drug discovery paradigms and then demonstrating their utility and reproducibility to industry. The collaboration between Vern Schramm's group at the Albert Einstein College of Medicine, USA and Peter Tyler at the Ferrier Research Institute at The Victoria University of Wellington, NZ has refined a drug discovery process called transition state analogue design. This process has been applied to several biomedically relevant nucleoside processing enzymes. In 2017, Mundesine®, conceived using transition state analogue design, received market approval for the treatment of peripheral T-cell lymphoma in Japan. This short review looks at a brief history of transition state analogue design, the fundamentals behind the development of this process, and the success of enzyme inhibitors produced using this drug design methodology.

Predicting Fraction Unbound in Human Plasma from Chemical Structure: Improved Accuracy in the Low Value Ranges

Predicting the fraction unbound in plasma provides a good understanding of the pharmacokinetic properties of a drug to assist candidate selection in the early stages of drug discovery. It is also an effective tool to mitigate the risk of late-stage attrition and to optimize further screening. In this study, we built in silico prediction models of fraction unbound in human plasma with freely available software, aiming specifically to improve the accuracy in the low value ranges. We employed several machine learning techniques and built prediction models trained on the largest ever data set of 2738 experimental values. The classification model showed a high true positive rate of 0.826 for the low fraction unbound class on the test set. The strongly biased distribution of the fraction unbound in plasma was mitigated by a logarithmic transformation in the regression model, leading to improved accuracy at lower values. Overall, our models showed better performance than those of previously published methods, including commercial software. Our prediction tool can be used on its own or integrated into other pharmacokinetic modeling systems.

The reproducibility issue and preclinical academic drug discovery: educational and institutional initiatives fostering translation success

INTRODUCTION

Drug discovery depends critically upon published results from the academy. The reproducibility of preclinical research findings reported by academia in the peer-reviewed literature has been called into question, seriously jeopardizing the value of academic science for inventing therapeutics.

AREAS COVERED

The corrosive effects of the reproducibility issue on drug discovery are considered. Purported correctives imposed upon academia from the outside deal mainly with expunging fraudulent literature and imposing punitive sanctions on the responsible authors. The salutary influence of such post facto actions on the reproducibility of discovery-relevant preclinical research data from academia appears limited. Rather, intentional doctoral-scientist education focused on data replicability and translationally-meaningful science and active participation of university entities charged with research innovation and asset commercialization toward ensuring data quality are advocated as key academic initiatives for addressing the reproducibility issue.

EXPERT OPINION

A mindset shift on the part of both senior university faculty and the academy to take responsibility for the data reproducibility crisis and commit proactively to positive educational, incentivization, and risk- and reward-sharing practices will be fundamental for improving the value of published preclinical academic research to drug discovery.

A cell-based screening assay to identify pharmaceutical compounds that enhance the regenerative quality of corneal repair

The goal of this study was to develop and validate a simple but quantitative cell-based assay to identify compounds that might be used pharmaceutically to give tissue repair a more regenerative character. The cornea was used as the model, and some specific aspects of repair in this organ were incorporated into assay design. A quantitative cell-based assay was developed based on transcriptional promoter activity of fibrotic marker genes ACT2A and TGFB2. Immortalized corneal stromal cells (HTK) or corneal epithelial cells (HCLE) were tested and compared to primary corneal stromal cells. Cells were transiently transfected with constructs containing the firefly luciferase reporter gene driven by transcriptional promoters for the selected fibrotic marker genes. A selected panel of seven chemical test compounds was used, containing three known fibrosis inhibitors: lovastatin (LOV), tyrphostin AG 1296 (6,7-dimethoxy-3-phenylquinoxaline) and SB203580 (4-(4-fluorophenyl)-2-(4-methylsulfinylphenyl)-5-(4-pyridyl)1H-imidazole), and four potential fibrosis inhibitors: 5-iodotubercidin (4-amino-5-iodo-7-(β-D-ribofuranosyl)-pyrrolo(2,3-d)pyrimidine), anisomycin, DRB (5,6-dichloro-1-β-D-ribofuranosyl-benzimidazole) and latrunculin B. Transfected cells were treated with TGFB2 in the presence or absence of one of the test compounds. To validate the assay, compounds were tested for their direct effects on gene expression in the immortalized cell lines and primary human corneal keratocytes using RT-PCR and immunohistochemistry. Three "hits" were validated LOV, SB203580 and anisomycin. This assay, which can be applied in a high throughput format to screen large libraries of uncharacterized compounds, or known compounds that might be repurposed, offers a valuable tool for identifying new treatments to address a major unmet medical need. Anisomycin has not previously been characterized as antifibrotic, thus, this is a novel finding of the study.