Determining recommended acceptable intake limits for N-nitrosamine impurities in pharmaceuticals: Development and application of the Carcinogenic Potency Categorization Approach (CPCA).

N-Nitrosamine impurities, including nitrosamine drug substance-related impurities (NDSRIs), have challenged pharmaceutical industry and regulators alike and affected the global drug supply over the past 5 years. Nitrosamines are a class of known carcinogens, but NDSRIs have posed additional challenges as many lack empirical data to establish acceptable intake (AI) limits. Read-across analysis from surrogates has been used to identify AI limits in some cases; however, this approach is limited by the availability of robustly-tested surrogates matching the structural features of NDSRIs, which usually contain a diverse array of functional groups. Furthermore, the absence of a surrogate has resulted in conservative AI limits in some cases, posing practical challenges for impurity control. Therefore, a new framework for determining recommended AI limits was urgently needed. Here, the Carcinogenic Potency Categorization Approach (CPCA) and its supporting scientific rationale are presented. The CPCA is a rapidly-applied structure-activity relationship-based method that assigns a nitrosamine to 1 of 5 categories, each with a corresponding AI limit, reflecting predicted carcinogenic potency. The CPCA considers the number and distribution of α-hydrogens at the N-nitroso center and other activating and deactivating structural features of a nitrosamine that affect the α-hydroxylation metabolic activation pathway of carcinogenesis. The CPCA has been adopted internationally by several drug regulatory authorities as a simplified approach and a starting point to determine recommended AI limits for nitrosamines without the need for compound-specific empirical data.

Assessing the impact of expert knowledge on ICH M7 (Q)SAR predictions. Is expert review still needed?

The ICH M7 (R1) guideline recommends the use of complementary (Q)SAR models to assess the mutagenic potential of drug impurities as a state-of-the-art, high-throughput alternative to empirical testing. Additionally, it includes a provision for the application of expert knowledge to increase prediction confidence and resolve conflicting calls. Expert knowledge, which considers structural analogs and mechanisms of activity, has been valuable when models return an indeterminate (equivocal) result or no prediction (out-of-domain). A retrospective analysis of 1002 impurities evaluated in drug regulatory applications between April 2017 and March 2019 assessed the impact of expert review on (Q)SAR predictions. Expert knowledge overturned the default predictions for 26% of the impurities and resolved 91% of equivocal predictions and 75% of out-of-domain calls. Of the 261 overturned default predictions, 15% were upgraded to equivocal or positive and 79% were downgraded to equivocal or negative. Chemical classes with the most overturns were primary aromatic amines (46%), aldehydes (45%), Michael-reactive acceptors (37%), and non-primary alkyl halides (33%). Additionally, low confidence predictions were the most often overturned. Collectively, the results suggest that expert knowledge continues to play an important role in an ICH M7 (Q)SAR prediction workflow and triaging predictions based on chemical class and probability can improve (Q)SAR review efficiency.

SAR of psilocybin analogs: discovery of a selective 5-HT 2C agonist.

An SAR study of psilocybin and psilocin derivatives reveals that 1-methylpsilocin is a selective agonist at the h5-HT(2C) receptor. The corresponding phosphate derivative, 1-methylpsilocybin, shows efficacy in an animal model for obsessive-compulsive disorder, as does 4-fluoro-N,N-dimethyltryptamine. These results suggest a new area for development of novel 5-HT(2C) agonists with applications for drug discovery.