Low-cost quantum mechanical descriptors for data efficient skin sensitization QSAR models.

Quantitative Structure Activity Relationship modelling methodologies need to incorporate relevant mechanistic information to have high predictive performance and validity. Electrophilic reactivity is a common mechanistic feature of skin sensitization endpoints which could be concisely characterized with electronic descriptors which is key to enabling the modelling of small datasets in this domain. However, quantum mechanical methodologies have previously featured high computational costs which would exclude the use of large datasets. Consequently, we investigate the use of electronic descriptors calculated using the Hartree Fock with 3 corrections (Hf-3c) method, a low-cost ab initio methodology that has higher chemical accuracy than previous semiempirical methodologies for modelling in vitro skin sensitization assay outcomes. We also model the Ames assay as a surrogate for determining skin sensitization outcomes. The quantum chemical descriptors calculated using the Hf-3c method with conductor-like polarizable continuum model (CPCM) implicit solvation found improved QSAR model performance for the in vitro Ames (n = 6049, 0.770 AUC), KeratinoSens (n = 164, 0.763 AUC), and Direct Peptide Reactivity Assay (n = 122, 0.750 AUC) datasets, with their combination producing high predictive performance for unseen in vivo Local Lymph Node Assay (n = 86, 0.789 AUC) and Human Repeated Insult Patch Test (n = 86, 0.791 AUC) assay toxicant outcomes.

Deus Ex Machina? The Rise of Artificial Intelligence in Toxicology.

Artificial intelligence (AI) is rising rapidly, driven by big data, complex algorithms, and computing resources. Current research presented at the American Chemical Society Fall 2023 Meeting demonstrates AI to be a valuable predictive and supporting tool across all facets of toxicology.

Multiple Instance Learning Improves Ames Mutagenicity Prediction for Problematic Molecular Species.

The prediction of Ames mutagenicity continues to be a concern in both regulatory and pharmacological toxicology. Traditional quantitative structure-activity relationship (QSAR) models of mutagenicity make predictions based on molecular descriptors calculated on a chemical data set used in their training. However, it is known that molecules such as aromatic amines can be non-mutagenic themselves but metabolically activated by S9 rodent liver enzyme in Ames tests forming molecules such as iminoquinones or amine substituents that better stabilize mutagenic nitrenium ions in known pathways of mutagenicity. Modern in silico modeling methods can implicitly model these metabolites through consideration of the structural elements relevant to their formation but do not include explicit modeling of these metabolites' potential activity. These metabolites do not have a known individual mutagenicity label and, in their current state, cannot be fitted into a traditional QSAR model. Multiple instance learning (MIL) however can be applied to a group of metabolites and their parent under a single mutagenicity label. Here we trained MIL models on Ames data, first with an aromatic amines data set (n = 457), a class known to require metabolic activation, and subsequently on a larger data set (n = 6505) incorporating multiple molecular species. MIL was shown to be able to predict Ames mutagenicity with performance in line with previously established models (balanced accuracy = 0.778), suggesting its potential utility in Ames prediction applications. Furthermore, the MIL model predicted well on identified hard-to-predict molecule groups relative to the models in which these molecule groups were identified. These results are presumably due to the increased consideration of the metabolic contribution to the mutagenic outcome. Further exploration of MIL as a supplement to existing models could aid in the prediction of chemicals where implicit modeling of metabolites cannot fully grasp their characteristics. This paper demonstrates the potential of an MIL approach to modeling Ames tests with S9 and is particularly relevant to metabolically activated xenobiotic mutagens.

Mechanistic Task Groupings Enhance Multitask Deep Learning of Strain-Specific Ames Mutagenicity.

The Ames test is a gold standard mutagenicity assay that utilizes various Salmonella typhimurium strains with and without S9 fraction to provide insights into the mechanisms by which a chemical can mutate DNA. Multitask deep learning is an ideal framework for developing QSAR models with multiple end points, such as the Ames test, as the joint training of multiple predictive tasks may synergistically improve the prediction accuracy of each task. This work investigated how toxicology domain knowledge can be used to handcraft task groupings that better guide the training of multitask neural networks compared to a naïve ungrouped multitask neural network developed on a complete set of tasks. Sixteen S. typhimurium ± S9 strain tasks were used to generate groupings based on mutagenic and metabolic mechanisms that were reflected in correlation data analyses. Both grouped and ungrouped multitask neural networks predicted the 16 strain tasks with a higher balanced accuracy compared with single task controls, with grouped multitask neural networks consistently featuring incremental increases in predictivity over the ungrouped approach. We conclude that the main variable driving these performance improvements is the general multitask effect with mechanistic task groupings acting as an enhancement step to further concentrate synergistic training signals united by a common biological mechanism. This approach enables incorporation of toxicology domain knowledge into multitask QSAR model development allowing for more transparent and accurate Ames mutagenicity prediction.

An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials.

The Open Source Malaria (OSM) consortium is developing compounds that kill the human malaria parasite, Plasmodium falciparum, by targeting PfATP4, an essential ion pump on the parasite surface. The structure of PfATP4 has not been determined. Here, we describe a public competition created to develop a predictive model for the identification of PfATP4 inhibitors, thereby reducing project costs associated with the synthesis of inactive compounds. Competition participants could see all entries as they were submitted. In the final round, featuring private sector entrants specializing in machine learning methods, the best-performing models were used to predict novel inhibitors, of which several were synthesized and evaluated against the parasite. Half possessed biological activity, with one featuring a motif that the human chemists familiar with this series would have dismissed as "ill-advised". Since all data and participant interactions remain in the public domain, this research project "lives" and may be improved by others.

A comparison of molecular representations for lipophilicity quantitative structure-property relationships with results from the SAMPL6 logP Prediction Challenge.

Effective representation of a molecule is required to develop useful quantitative structure-property relationships (QSPR) for accurate prediction of chemical properties. The octanol-water partition coefficient logP, a measure of lipophilicity, is an important property for pharmacological and toxicological endpoints used in the pharmaceutical and regulatory spheres. We compare physicochemical descriptors, structural keys, and circular fingerprints in their ability to effectively represent a chemical space and characterise molecular features to correlate with lipophilicity. Exploratory landscape continuity analyses revealed that whole-molecule physicochemical descriptors could map together compounds that were similar in both molecular features and logP, indicating higher potential for use in logP QSPRs compared to the substructural approach of structural keys and circular fingerprints. Indeed, logP QSPR models parameterised by physicochemical descriptors consistently performed with the lowest error. Our best performing model was a stochastic gradient descent-optimised multilinear regression with 1438 descriptors, returning an internal benchmark RMSE of 1.03 log units. This corroborates the well-established notion that lipophilicity is an additive, whole-molecule property. We externally tested the model by participating in the 2019 SAMPL6 logP Prediction Challenge and blindly predicting for 11 protein kinase inhibitor fragment-like molecules. Our model returned an RMSE of 0.49 log units, placing eighth overall and third in the empirical methods category (submission ID 'hdpuj'). Permutation feature importance analyses revealed that physicochemical descriptors could characterise predictive molecular features highly relevant to the kinase inhibitor fragment-like molecules.

LogP prediction performance with the SMD solvation model and the M06 density functional family for SAMPL6 blind prediction challenge molecules.

This work presents a quantum mechanical model for predicting octanol-water partition coefficients of small protein-kinase inhibitor fragments as part of the SAMPL6 LogP Prediction Challenge. The model calculates solvation free energy differences using the M06-2X functional with SMD implicit solvation and the def2-SVP basis set. This model was identified as dqxk4 in the SAMPL6 Challenge and was the third highest performing model in the physical methods category with 0.49 log Root Mean Squared Error (RMSE) for predicting the 11 compounds in SAMPL6 blind prediction set. We also collaboratively investigated the use of empirical models to address model deficiencies for halogenated compounds at minimal additional computational cost. A mixed model consisting of the dqxk4 physical and hdpuj empirical models found improved performance at 0.34 log RMSE on the SAMPL6 dataset. This collaborative mixed model approach shows how empirical models can be leveraged to expediently improve performance in chemical spaces that are difficult for ab initio methods to simulate.

The impact of a culturally tailored patient navigator program on cervical cancer prevention in Latina women.

BACKGROUND: Cervical cancer disproportionately affects Latina women in the United States. This study evaluated the impact of patient navigation on cervical cancer prevention in Latinas. METHODS: Between January 2004 and April 2011, 533 Latina women with an abnormal Pap smear requiring colposcopy received patient navigation from their healthcare center in Chelsea, Massachusetts, to the Massachusetts General Hospital (MGH). The comparison group comprised 253 non-navigated Latinas from other primary care practices at MGH referred to the same MGH colposcopy clinic. Primary outcomes were the percentage of missed colposcopy appointments, time to colposcopy, and changes in the severity of cervical pathology at colposcopy over two time periods, 2004-2007 and 2008-2011. RESULTS: The mean age in both groups was 35 years (range 22-86). Navigated women had fewer missed colposcopy appointments over time, with the average falling from 19.8% to 15.7% (p=0.024), compared with an insignificant increase in the no-show rates from 18.6% to 20.6% (p=0.454) in the comparison group. The difference in the no-show rate trend over time between the groups was significant (p<0.001). The time to colposcopy did not change in either group, though trends over time demonstrated a shorter follow-up for navigated women (p=0.010). The grade of cervical abnormality among navigated women decreased from a numerical score of 2.03 to 1.83 (p=0.035) over the two time intervals, while the severity of pathological score in the non-navigated group did not change significantly from 1.83 to 1.92 (p=0.573) in the same interval. Comparison of trends in pathological score over time showed a decrease in the severity of cervical abnormality for navigated participants compared to the non-navigated group (p<0.001). CONCLUSION: Patient navigation can prevent cervical cancer in Latina women by increasing colposcopy clinic attendance, shortening time to colposcopy, and decreasing severity of cervical abnormalities over time.

Clinical utility of HPV testing.

Testing for human papilloma virus (HPV) has been shown to be more sensitive than cervical cytology in detecting both high-grade and low-grade dysplasia. When screening for cervical cancer, unfortunately, the HPV test lacks specificity and has limited its usefulness as a primary screening modality for cervical cancer. In this chapter, we will review HPV and its role in cervical cancer, the utilization of HPV testing in current practice, and the possible future utilization of HPV and its role in screening.