Morrison, P. | Search bioRxiv

bioRxiv2019

Predicting failures of Molteno and Baerveldt glaucoma drainage devices using machine learning models

The purpose of this retrospective study is to measure machine learning models ability to predict glaucoma drainage device failure based on demographic information and preoperative measurements. The medical records of sixty-two patients were used. Potential predictors included the patients race, age, sex, preoperative intraocular pressure, preoperative visual acuity, number of intraocular pressure-lowering medications, and number and type of previous ophthalmic surgeries. Failure was defined as final intraocular pressure greater than 18 mm Hg, reduction in intraocular pressure less than 20% from baseline, or need for reoperation unrelated to normal implant maintenance. Five classifiers were compared: logistic regression, artificial neural network, random forest, decision tree, and support vector machine. Recursive feature elimination was used to shrink the number of predictors and grid search was used to choose hyperparameters. To prevent leakage, nested cross-validation was used throughout. Overall, the best classifier was logistic regression.

bioinformatics↗

bioRxiv2019

Human loss-of-function variants suggest that partial LRRK2 inhibition is a safe therapeutic strategy for Parkinsons disease

Human genetic variants causing loss-of-function (LoF) of protein-coding genes provide natural in vivo models of gene inactivation, which are powerful indicators of gene function and the potential toxicity of therapeutic inhibitors targeting these genes1,2. Gain-of-kinase-function variants in LRRK2 are known to significantly increase the risk of Parkinsons disease3,4, suggesting that inhibition of LRRK2 kinase activity is a promising therapeutic strategy. Whilst preclinical studies in model organisms have raised some on-target toxicity concerns5-8, the biological consequences of LRRK2 inhibition have not been well characterized in humans. Here we systematically analyse LoF variants in LRRK2 observed across 141,456 individuals sequenced in the Genome Aggregation Database (gnomAD)9 and over 4 million participants in the 23andMe genotyped dataset, to assess their impact at a molecular and phenotypic level. After thorough variant curation, we identify 1,358 individuals with high-confidence predicted LoF variants in LRRK2, several with experimental validation. We show that heterozygous LoF of LRRK2 reduces LRRK2 protein level by ~50% but is not associated with reduced life expectancy, or with any specific phenotype or disease state. These data suggest that therapeutics that downregulate LRRK2 levels or kinase activity by up to 50% are unlikely to have major on-target safety liabilities. Our results demonstrate the value of large scale genomic databases and phenotyping of human LoF carriers for target validation in drug discovery.

genomics↗