| 1. |
- Scott, James S., et al.
(författare)
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Circumventing seizure activity in a series of G protein coupled receptor 119 (GPR119) agonists
- 2014
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Ingår i: Journal of Medicinal Chemistry. - : American Chemical Society. - 0022-2623 .- 1520-4804. ; 57:21, s. 8984-8998
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Tidskriftsartikel (refereegranskat)abstract
- Agonism of GPR119 is viewed as a potential therapeutic approach for the treatment of type II diabetes and other elements of metabolic syndrome. During progression of a previously disclosed candidate 1 through mice toxicity studies, we observed tonic-clonic convulsions in several mice at high doses. An in vitro hippocampal brain slice assay was used to assess the seizure liability of subsequent compounds, leading to the identification of an aryl sulfone as a replacement for the 3-cyano pyridyl group. Subsequent optimization to improve the overall profile, specifically with regard to hERG activity, led to alkyl sulfone 16. This compound did not cause tonic-clonic convulsions in mice, had a good pharmacokinetic profile, and displayed in vivo efficacy in murine models. Importantly, it was shown to be effective in wild-type (WT) but not GPR119 knockout (KO) animals, consistent with the pharmacology observed being due to agonism of GPR119.
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| 2. |
- Wallert, John, et al.
(författare)
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Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data
- 2022
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Ingår i: Translational Psychiatry. - : Springer Nature. - 2158-3188. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- This study applied supervised machine learning with multi-modal data to predict remission of major depressive disorder {MDD) after psychotherapy. Genotyped adult patients (n = 894, 65.5% women, age 18-75 years) diagnosed with mild-to-moderate MDD and treated with guided Internet-based Cognitive Behaviour Therapy (ICBT) at the Internet Psychiatry Clinic in Stockholm were included (2008-2016). Predictor types were demographic, clinical, process (e.g., time to complete online questionnaires), and genetic (polygenic risk scores). Outcome was remission status post ICBT (cut-off <= 10 on MADRS-S). Data were split into train (60%) and validation (40%) given ICBT start date. Predictor selection employed human expertise followed by recursive feature elimination. Model derivation was internally validated through cross-validation. The final random forest model was externally validated against a (i) null, (ii) logit, (iii) XGBoost, and {iv) blended meta-ensemble model on the hold-out validation set. Feature selection retained 45 predictors representing all four predictor types. With unseen validation data, the final random forest model proved reasonably accurate at classifying post ICBT remission (Accuracy 0.656 [0.604, 0.705], P vs null model = 0.004; AUC 0.687 [0.631, 0.743]), slightly better vs logit (bootstrap D = 1.730, P = 0.084) but not vs XGBoost (D = 0.463, P = 0.643). Transparency analysis showed model usage of all predictor types at both the group and individual patient level. A new, multi-modal classifier for predicting MDD remission status after ICBT treatment in routine psychiatric care was derived and empirically validated. The multi-modal approach to predicting remission may inform tailored treatment, and deserves further investigation to attain clinical usefulness.
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