Biology subjects

Arias, W.

Publications and source records attributed to Arias, W..

2 recordsLinked to original sources

bioRxiv2024

A Systematic Assessment of Robustness in CNS Safety Pharmacology

Irwin tests are key preclinical study elements for characterizing drug-induced neurological side effects. This multicenter study aimed to assess the robustness of Irwin tests across multinational sites during three stages of protocol harmonization. The projects were part of the EQIPD framework (Enhanced Quality in Preclinical Data, https://quality-preclinical-data.eu/), aiming to increase success rates in transition from preclinical testing to clinical application. Female and male NMRI mice were assigned to one of three groups (vehicle, 0.1 mg/kg MK-801, 0.3 mg/kg MK-801). Irwin scores were assessed at baseline and multiple times following injection of MK-801, a non-competitive NMDA antagonist, using local protocols (stage 1), a shared protocol with harmonized environmental design (stage 2), and fully harmonized Irwin scoring protocols (stage 3). The analysis based on the four functional domains (motor, autonomic, sedation, and excitation) revealed substantial data variability in stages 1 and 2. Although there was still marked overall heterogeneity between sites in stage 3 after complete harmonization of the Irwin scoring scheme, heterogeneity was only moderate within functional domains. When comparing treatment groups vs. vehicle, we found large effect sizes in the motor domain and subtle to moderate effects in the excitation-related and autonomic domain. The pronounced interlaboratory variability in Irwin datasets for the CNS-active compound MK-801 needs to be carefully considered by companies and experimenters when making decisions during drug development. While environmental and general study design had a minor impact, the study suggests that harmonization of parameters and their scoring can limit variability and increase robustness.

neuroscience↗

bioRxiv2020

Prediction Of Eye, Hair And Skin Color In Admixed Populations Of Latin America

We report an evaluation of prediction accuracy for eye, hair and skin pigmentation based on genomic and phenotypic data for over 6,500 admixed Latin Americans (the CANDELA dataset). We examined the impact on prediction accuracy of three main factors: (i) The methods of prediction, including classical statistical methods and machine learning approaches, (ii) The inclusion of non-genetic predictors, continental genetic ancestry and pigmentation SNPs in the prediction models, and (iii) Compared two sets of pigmentation SNPs: the commonly-used HIrisPlex-S set (developed in Europeans) and novel SNP sets we defined here based on genome-wide association results in the CANDELA sample. We find that Random Forest or regression are globally the best performing methods. Although continental genetic ancestry has substantial power for prediction of pigmentation in Latin Americans, the inclusion of pigmentation SNPs increases prediction accuracy considerably, particularly for skin color. For hair and eye color, HIrisPlex-S has a similar performance to the CANDELA-specific prediction SNP sets. However, for skin pigmentation the performance of HIrisPlex-S is markedly lower than the SNP set defined here, including predictions in an independent dataset of Native American data. These results reflect the relatively high variation in hair and eye color among Europeans for whom HIrisPlex-S was developed, whereas their variation in skin pigmentation is comparatively lower. Furthermore, we show that the dataset used in the training of prediction models strongly impacts on the portability of these models across Europeans and Native Americans.

genetics↗