In silico toxicology protocols

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information

Establishing best practise in the application of expert review of mutagenicity under ICH M7

The ICH M7 guidelines for the assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals allows for the consideration of in silico predictions in place of in vitro studies. This represents a significant advance in the acceptance of (Q)SAR models and has resulted from positive interactions between modellers, regulatory agencies and industry with a shared purpose of developing effective processes to minimise risk. This paper discusses key scientific principles that should be applied when evaluating in silico predictions with a focus on accuracy and scientific rigour that will support a consistent and practical route to regulatory submission

Acceptable Intakes (AIs) for 11 Simple N-Nitrosamine (NA) Impurities

Low levels of N-nitrosamine (NA) impurities were detected in pharmaceuticals and, as a result, health authorities (HAs) have developed acceptable intakes (AI) as a protective control on the level of NA permissible in pharmaceuticals. Eleven common NA impurities were analyzed in a comprehensive and transparent toxicological process consistent with ICH M7 and compared to the AIs developed by HAs. This comprehensive analysis included substances which had datasets which were robust, limited but sufficient, and substances with insufficient carcinogenicity data. In the case of robust or limited but sufficient carcinogenicity information, AIs were calculated based on published or derived TD50’s from the most sensitive organ site. In the case of insufficient carcinogenicity information, available carcinogenicity data and structure activity relationship (SAR) was used and applied to existing thresholds of 1500 ng/day, 150 ng/day or 18 ng/day. This approach advances the methodology used to derive AIs for NA impurities

"Principles and procedures for implementation of ICH M7 recommended (Q)SAR analyses"

The ICH M7 guideline describes a consistent approach to identify, categorize, and control DNA reactive impurities in pharmaceutical products to limit the potential carcinogenic risk related to such impurities. This paper outlines a series of principles and procedures to consider when generating (Q)SAR assessments aligned with the ICH M7 guideline to be included in a regulatory submission. In the absence of adequate experimental data, the results from two complementary (Q)SAR methodologies may be combined to support an initial hazard classification. This may be followed by an assessment of additional information that serves as the basis for an expert opinion to support or refute the predictions. This paper elucidates scenarios where additional expert knowledge may be beneficial, what such an opinion may contain, and how the results and accompanying considerations may be documented. Furthermore, the use of these principles and procedures to yield a consistent and robust (Q)SAR-based argument to support impurity qualification for regulatory purposes is described in this manuscript

Practical experience of in silico assessment for genotoxic impurities

Novartis contribution on in silico assessment of impurities to a survey paper to which different Pharma companies contribut

Management of pharmaceutical ICH M7 (Q)SAR predictions – The impact of model updates

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

Qualification of impurities based on metabolite data

Regulatory Guidance documents ICH Q3A (R2) and ICH Q3B (R2) state that “impurities that are also significant metabolites present in animal and/or human studies are generally considered qualified”. However, no guidance is provided regarding data requirements for qualification, nor is a definition of the term “significant metabolite” provided. An opportunity is provided to define those categories and potentially avoid separate toxicity studies to qualify impurities. This can reduce cost, animal use and time, and avoid delays in drug development progression. If the concentration or amount of a metabolite, in animals or human, is similar to that of the known, structurally identical impurity (arising from the administered test material), the qualification of the impurity on the grounds of it also being a metabolite is justified. We propose two complementary approaches to support conclusions to this effect: 1) demonstrate that the impurity is formed by metabolism in animals and/or man, based preferably on plasma exposures or, alternatively, amounts excreted in urine, and, where appropriate, 2) show that animal exposure to (or amount of) the impurity/metabolite is equal or greater in animals than in humans. An important factor of both assessments is the maximum theoretical concentration (or amount) (MTC or MTA) of the impurity/metabolite achievable from the administered dose and recommendations on the estimation of the MTC and MTA are presented

The new ICH M7 Guideline on the assessment and control of mutagenic impurities in pharmaceuticals – Implications on its practical use

The new ICH M7 Guideline "ASSESSMENT AND CONTROL OF DNA REACTIVE (MUTAGENIC) IMPURITIES IN PHARMACEUTICALS TO LIMIT POTENTIAL CARCINOGENIC RISK“ provides guidance on safety and quality risk management in establishing levels of mutagenic impurities that are expected to pose negligible carcinogenic risk. It outlines recommendations for assessment and control of mutagenic impurities that reside or are reasonably expected to reside in final drug substance or product, taking into consideration the intended conditions of human use. The paper describes the basic requirements of the guideline from a safety and an analytical view point

Intermediates mutagenicity data sharing for 2017

This is the yearly intemediates mutagenicity data sharing for the impurity consortium. Only small chemicals used as reagents in synthesis are used. All are unrelated to the drug substance and have a CAS unmber. The data will go into a database that is shared between the consortium members from pharmaceutical industry, so we benefit from the donation of the other members

Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods

Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD50) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD50-based acute toxicity for the purpose of GHS classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD50, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity