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.

Medicines discovery for auditory disorders: Challenges for industry

Currently, no approved medicines are available for the prevention or treatment of hearing loss. Pharmaceutical industry productivity across all therapeutic indications has historically been disappointing, with a 90% chance of failure in delivering a marketed drug after entering clinical evaluation. To address these failings, initiatives have been applied in the three cornerstones of medicine discovery: target selection, clinical candidate selection, and clinical studies. These changes aimed to enable data-informed decisions on the translation of preclinical observations into a safe, clinically effective medicine by ensuring the best biological target is selected, the most appropriate chemical entity is advanced, and that the clinical studies enroll the correct patients. The specific underlying pathologies need to be known to allow appropriate patient selection, so improved diagnostics are required, as are methodologies for measuring in the inner ear target engagement, drug delivery and pharmacokinetics. The different therapeutic strategies of protecting hearing or preventing hearing loss versus restoring hearing are reviewed along with potential treatments for tinnitus. Examples of current investigational drugs are discussed to highlight key challenges in drug discovery and the learnings being applied to improve the probability of success of launching a marketed medicine.

The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization

BACKGROUND

Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug.

METHODS

In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan.

RESULTS

A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given.

CONCLUSION

The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.

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.

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.

CNS Anticancer Drug Discovery and Development: 2016 conference insights

CNS Anticancer Drug Discovery and Development, 16-17 November 2016, Scottsdale, AZ, USA The 2016 second CNS Anticancer Drug Discovery and Development Conference addressed diverse viewpoints about why new drug discovery/development focused on CNS cancers has been sorely lacking. Despite more than 70,000 individuals in the USA being diagnosed with a primary brain malignancy and 151,669-286,486 suffering from metastatic CNS cancer, in 1999, temozolomide was the last drug approved by the US FDA as an anticancer agent for high-grade gliomas. Among the topics discussed were economic factors and pharmaceutical risk assessments, regulatory constraints and perceptions and the need for improved imaging surrogates of drug activity. Included were modeling tumor growth and drug effects in a medical environment in which direct tumor sampling for biological effects can be problematic, potential new drugs under investigation and targets for drug discovery and development. The long trajectory and diverse impediments to novel drug discovery, and expectation that more than one drug will be needed to adequately inhibit critical intracellular tumor pathways were viewed as major disincentives for most pharmaceutical/biotechnology companies. While there were a few unanimities, one consensus is the need for continued and focused discussion among academic and industry scientists and clinicians to address tumor targets, new drug chemistry, and more time- and cost-efficient clinical trials based on surrogate end points.

Unusual Undergraduate Training in Medicinal Chemistry in Collaboration between Academia and Industry

Globalization has driven new paradigms for drug discovery and development. Activities previously carried out predominantly in the United States, Europe, and Japan are now carried out globally. This has caused considerable change in large pharma including how medicinal chemists are trained. Described here is the training of chemistry undergraduates in medicinal chemistry (as practiced in industry) in two modules developed in collaboration between the University of Nottingham (UoN) and GlaxoSmithKline (GSK). The students complete several design-synthesize-test iterations on medicinal chemistry projects where they carry out the design and synthesis, and GSK tests the compounds. Considerable emphasis is placed on standard design properties used within industry. The modules are popular with the students and usually oversubscribed. An unexpected benefit has been the opportunities that have emerged with research and commercial potential. Graduate and postgraduate training of medicinal chemists at GSK is also briefly described.