Publications by authors named "Angelina Hu"
Article Synopsis
- Current estimates of genetic variants linked to Parkinson's disease (PD) show limitations and biases across different populations, complicating patient recruitment for clinical trials focused on genetic therapies.
- The Rostock Parkinson's disease (ROPAD) study analyzes data from 12,580 PD patients across 16 countries, revealing that 14.8% had a genetic test positive for PD-related variants, particularly in specific genes like GBA1 and LRRK2.
- Findings indicate higher positivity rates in patients with earlier onset (age ≤ 50) or a positive family history, emphasizing the need for more extensive genetic investigation to improve patient stratification for future clinical trials.
Cytochrome P450 (P450)-mediated bioactivation, which can lead to the hepatotoxicity through the formation of reactive metabolites (RMs), has been regarded as the major problem of drug failures. Herein, we purposed to establish machine learning models to predict the bioactivation of P450. On the basis of the literature-derived bioactivation dataset, models for Benzene ring, Nitrogen heterocycle and Sulfur heterocycle were developed with machine learning methods, i.
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- UDP-glucuronosyltransferase 1A1 (UGT1A1) is crucial for detoxifying various substances, making its metabolic profile important for avoiding drug interactions.
- The study developed machine learning models to predict the metabolism of UGT1A1 substrates, using eight methods, with Random Forest, Random Subspace, and J48 performing the best.
- The models showed high accuracy with AUC values between 0.901 and 0.997 and successfully predicted new metabolites, providing a solid strategy for enhancing drug metabolism and minimizing clinical drug-drug interactions.