In silico toxicology (IST) approaches to rapidly assess chemical hazard, and usage of such methods is increasing in all applications but especially for regulatory submissions, such as for assessing chemicals under REACH as well as the ICH M7 guideline for drug impurities. There are a number of obstacles to performing an IST assessment, including uncertainty in how such an assessment and associated expert review should be performed or what is fit for purpose, as well as a lack of confidence that the results will be accepted by colleagues, collaborators and regulatory authorities. To address this, a project to develop a series of IST protocols for different hazard endpoints has been initiated and this paper describes the genetic toxicity in silico (GIST) protocol. The protocol outlines a hazard assessment framework including key effects/mechanisms and their relationships to endpoints such as gene mutation and clastogenicity. IST models and data are reviewed that support the assessment of these effects/mechanisms along with defined approaches for combining the information and evaluating the confidence in the assessment. This protocol has been developed through a consortium of toxicologists, computational scientists, and regulatory scientists across several industries to support the implementation and acceptance of in silico approaches.

Pharmaceutical applicants conduct (Q)SAR assessments on identified and theoretical impurities to predict their mutagenic potential. Two complementary models-one rule-based and one statistical-based-are used, followed by expert review. (Q)SAR models are continuously updated to improve predictions, with new versions typically released on a yearly basis. Numerous releases of (Q)SAR models will occur during the typical 6-7 years of drug development until new drug registration. Therefore, it is important to understand the impact of model updates on impurity mutagenicity predictions over time. Compounds representative of pharmaceutical impurities were analyzed with three rule- and three statistical-based models covering a 4-8 year period, with the individual time frame being dependent on when the individual models were initially made available. The largest changes in the combined outcome of two complementary models were from positive or equivocal to negative and from negative to equivocal. Importantly, the cumulative change of negative to positive predictions was small in all models (<5%) and was further reduced when complementary models were combined in a consensus fashion. We conclude that model updates of the type evaluated in this manuscript would not necessarily require re-running a (Q)SAR prediction unless there is a specific need. However, original (Q)SAR predictions should be evaluated when finalizing the commercial route of synthesis for marketing authorization.

The threshold of toxicological concern (TTC), i.e., the dose of a compound lacking sufficient experimental toxicity data that is unlikely to result in an adverse health effect in humans, is important for evaluating extractables and leachables (E&Ls) as it guides analytical testing and minimizes the use of animal studies. The ELSIE consortium, which consists of member companies that span biotechnology, pharmaceutical, and medical device industries, brought together subject matter expert toxicologists to derive TTC values for organic, non-mutagenic E&L substances when administered parenterally. A total of 488 E&L compounds from the ELSIE database were analyzed and parenteral point of departure (PPOD) estimates were derived for 252 compounds. The PPODs estimates were adjusted to extrapolate to subacute, subchronic, and chronic durations of nonclinical exposure and the lower 5th percentiles were calculated. An additional 100-fold adjustment factor to account for nonclinical species and human variability was subsequently applied to derive the parenteral TTC values for E&Ls. The resulting parenteral TTC values are 35, 110, and 180 μg/day for human exposures of >10 years to lifetime, >1-10 years, and ≤1 year, respectively. These parenteral TTCs are expected to be conservative for E&Ls that are considered non-mutagenic per ICH M7(R1) guidelines.

Due to challenges with historical data and the diversity of assay formats, in silico models for safety-related endpoints are often based on discretized data instead of the data on a natural continuous scale. Models for discretized endpoints have limitations in usage and interpretation that can impact compound design. Here, we present a consistent data inference approach, exemplified on two data sets of Ether-à-go-go-Related Gene (hERG) K+ inhibition data, for dose–response and screening experiments that are generally applicable for in vitro assays. hERG inhibition has been associated with severe cardiac effects and is one of the more prominent safety targets assessed in drug development, using a wide array of in vitro and in silico screening methods. In this study, the IC₅₀ for hERG inhibition is estimated from diverse historical proprietary data. The IC₅₀ derived from a two-point proprietary screening data set demonstrated high correlation (R = 0.98, MAE = 0.08) with IC_50s derived from six-point dose–response curves. Similar IC₅₀ estimation accuracy was obtained on a public thallium flux assay data set (R = 0.90, MAE = 0.2). The IC₅₀ data were used to develop a robust quantitative model. The model’s MAE (0.47) and R² (0.46) were on par with literature statistics and approached assay reproducibility. Using a continuous model has high value for pharmaceutical projects, as it enables rank ordering of compounds and evaluation of compounds against project-specific inhibition thresholds. This data inference approach can be widely applicable to assays with quantitative readouts and has the potential to impact experimental design and improve model performance, interpretation, and acceptance across many standard safety endpoints.

ICH Q3A/B guidelines provide qualification thresholds for impurities or degradation products in new drug substances and products. However, the guidelines note that certain impurities / degradation products may warrant further safety evaluation for being unusually potent or toxic. The purpose of this study was to confirm that especially toxic non-mutagenic compounds are rare and to identify classes of compounds that could warrant lower qualification thresholds. A total of 2,815 compounds were evaluated, of which 2,213 were assessed as non-mutagenic. For the purpose of this analysis, compounds were considered potent when the point of departure was ≤ 0.2 mg/kg/day based on the qualification threshold (1 mg/day or 0.02 mg/kg/day for a 50 kg human) in a new drug substance, with an additional 10-fold margin. Only 54 of the entire set (2.4%) would be considered potent based on this conservative potency analysis, confirming that the existing ICH Q3A/B qualification thresholds are appropriate for the majority of impurities. If the Q3A/B threshold, without the additional 10-fold margin is used, 14 compounds (0.6%) are considered "highly potent". Very few non-mutagenic structural classes were identified, including organothiophosphates and derivatives, polychlorinated benzenes and polychlorinated polycyclic aliphatics, that correlate with potential high potency, consistent with prior publications.