Mitochondrial dysfunction heightens the integrated stress response to drive ALS pathogenesis.

Vesicle-associated membrane protein-associated protein-B (VAPB) is an ER membrane bound protein. VAPB P56S causes a dominant, familial form of amyotrophic lateral sclerosis (ALS), however, the mechanism through which this mutation causes motor neuron (MN) disease remains unknown. Using inducible wild type (WT) and VAPB P56S expressing iPSC-derived MNs we show that VAPB P56S, but not WT, protein decreased neuronal firing and mitochondrial-ER contact (MERC) with an associated age-dependent decrease in mitochondrial membrane potential (MMP); all typical characteristics of MN-disease. We further show that VAPB P56S expressing iPSC-derived MNs have enhanced age-dependent sensitivity to ER stress. We identified elevated expression of the master regulator of the Integrated Stress Response (ISR) marker ATF4 and decreased protein synthesis in the VAPB P56S iPSC-derived MNs. Chemical inhibition of ISR with the compound, ISRIB, rescued all MN disease phenotype in VAPB P56S MNs. Thus, our results not only support ISR inhibition as a potential therapeutic target for ALS patients, but also provides evidence to pathogenesis.

Development of improved QSAR models for predicting the outcome of the in vivo micronucleus genetic toxicity assay.

All drugs entering clinical trials are expected to undergo a series of in vitro and in vivo genotoxicity tests as outlined in the International Council on Harmonization (ICH) S2 (R1) guidance. Among the standard battery of genotoxicity tests used for pharmaceuticals, the in vivo micronucleus assay, which measures the frequency of micronucleated cells mostly from blood or bone marrow, is recommended for detecting clastogens and aneugens. (Quantitative) structure-activity relationship [(Q)SAR] models may be used as early screening tools by pharmaceutical companies to assess genetic toxicity risk during drug candidate selection. Models can also provide decision support information during regulatory review as part of the weight-of-evidence when experimental data are insufficient. In the present study, two commercial (Q)SAR platforms were used to construct in vivo micronucleus models from a recently enhanced in-house database of non-proprietary study findings in mice. Cross-validated performance statistics for the new models showed sensitivity of up to 74% and negative predictivity of up to 86%. In addition, the models demonstrated cross-validated specificity of up to 77% and coverage of up to 94%. These new models will provide more reliable predictions and offer an investigational approach for drug safety assessment with regards to identifying potentially genotoxic compounds.

Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses.

The International Council on Harmonisation (ICH) M7(R1) guideline describes the use of complementary (quantitative) structure-activity relationship ((Q)SAR) models to assess the mutagenic potential of drug impurities in new and generic drugs. Historically, the CASE Ultra and Leadscope software platforms used two different statistical-based models to predict mutations at G-C (guanine-cytosine) and A-T (adenine-thymine) sites, to comprehensively assess bacterial mutagenesis. In the present study, composite bacterial mutagenicity models covering multiple mutation types were developed. These new models contain more than double the number of chemicals (n = 9,254 and n = 13,514) than the corresponding non-composite models and show better toxicophore coverage. Additionally, the use of a single composite bacterial mutagenicity model simplifies impurity analysis in an ICH M7 (Q)SAR workflow by reducing the number of model outputs requiring review. An external validation set of 388 drug impurities representing proprietary pharmaceutical chemical space showed performance statistics ranging from of 66-82% in sensitivity, 91-95% in negative predictivity and 96% in coverage. This effort represents a major enhancement to these (Q)SAR models and their use under ICH M7(R1), leading to improved patient safety through greater predictive accuracy, applicability, and efficiency when assessing the bacterial mutagenic potential of drug impurities.