CSL-Tox: an open-source analytical framework for the comparison of short-term and long-term toxicity end points and assessing the need of chronic studies in drug development.

In-vivo toxicity assessment is an important step prior to clinical development and is still the main source of data for overall risk assessment of a new molecular entity (NCE). All in-vivo studies are performed according to regulatory requirements and many efforts have been exerted to minimize these studies in accordance with the (Replacement, Reduction and Refinement) 3Rs principle. Many aspects of in-vivo toxicology packages can be optimized to reduce animal use, including the number of studies performed as well as study durations, which is the main focus of this analysis. We performed a statistical comparison of adverse findings observed in 116 short-term versus 78 long-term in-house or in-house sponsored Contract Research Organizations (CRO) studies, in order to explore the possibility of using only short-term studies as a prediction tool for the longer-term effects. All the data analyzed in this study was manually extracted from the toxicology reports (in PDF formats) to construct the dataset. Annotation of treatment related findings was one of the challenges faced during this work. A specific focus was therefore put on the summary and conclusion sections of the reports since they contain expert assessments on whether the findings were considered adverse or were attributed to other reasons. Our analysis showed a general good concordance between short-term and long-term toxicity findings for large molecules and the majority of small molecules. Less concordance was seen for certain body organs, which can be named as "target organ systems' findings". While this work supports the minimization of long-term studies, a larger-scale effort would be needed to provide more evidence. We therefore present the steps performed in this study as an open-source R workflow for the Comparison of Short-term and Long-term Toxicity studies (CSL-Tox). The dataset used in the work is provided to allow researchers to reproduce such analysis, re-evaluate the statistical tools used and promote large-scale application of this study. Important aspects of animal research reproducibility are highlighted in this work, specifically, the necessity of a reproducible adverse effects reporting system and utilization of the controlled terminologies in-vivo toxicology reports and finally the importance of open-source analytical workflows that can be assessed by other scientists in the field of preclinical toxicology.

Off-targetP ML: an open source machine learning framework for off-target panel safety assessment of small molecules.

Unpredicted drug safety issues constitute the majority of failures in the pharmaceutical industry according to several studies. Some of these preclinical safety issues could be attributed to the non-selective binding of compounds to targets other than their intended therapeutic target, causing undesired adverse events. Consequently, pharmaceutical companies routinely run in-vitro safety screens to detect off-target activities prior to preclinical and clinical studies. Hereby we present an open source machine learning framework aiming at the prediction of our in-house 50 off-target panel activities for ~ 4000 compounds, directly from their structure. This framework is intended to guide chemists in the drug design process prior to synthesis and to accelerate drug discovery. We also present a set of ML approaches that require minimum programming experience for deployment. The workflow incorporates different ML approaches such as deep learning and automated machine learning. It also accommodates popular issues faced in bioactivity predictions, as data imbalance, inter-target duplicated measurements and duplicated public compound identifiers. Throughout the workflow development, we explore and compare the capability of Neural Networks and AutoML in constructing prediction models for fifty off-targets of different protein classes, different dataset sizes, and high-class imbalance. Outcomes from different methods are compared in terms of efficiency and efficacy. The most important challenges and factors impacting model construction and performance in addition to suggestions on how to overcome such challenges are also discussed.

Pharmaceutical industry perspective on combination toxicity studies: Results from an intra-industry survey conducted by IQ DruSafe Leadership Group.

Interest in developing combination products to overcome drug resistance and treat complex diseases is growing. However, ambiguity remains around the value of combination toxicity studies to support combination products. Therefore, the IQ* DruSafe Leadership Group surveyed member companies to evaluate industry experience with combination toxicity strategies, study designs and their impact on clinical development. Twenty companies responded, representing 79 combination programs. Combination toxicity studies were performed based on scientific rationale, regulatory agency request, or expected regulatory requirement. Combination toxicity study designs were varied (eg, group numbers, dose selection rationale and endpoints assessed) with no evidence that any one study design was superior. Studies were perceived as adding value when they fulfilled a regulatory requirement; avoided potential development delays; or when new or exaggerated toxicity or pharmacokinetic interactions were identified. Twelve percent of combination toxicity studies impacted clinical trial designs. The decision to conduct and the design of nonclinical combination toxicity studies should be based on sound scientific judgement with proactive engagement with regulatory agencies. Studies are not warranted when sufficient knowledge (eg, expected pharmacology, known mechanism of action, drug disposition, toxicity profile) is available to proceed safely in clinical development.