Functional analysis of thirty-four suspected pathogenic missense variants in ALDH5A1 gene associated with succinic semialdehyde dehydrogenase deficiency.

Deficiency of succinate semialdehyde dehydrogenase (SSADH; aldehyde dehydrogenase 5a1 (ALDH5A1), OMIM 271980, 610045), the second enzyme of GABA degradation, represents a rare autosomal-recessively inherited disorder which manifests metabolically as gamma-hydroxybutyric aciduria. The neurological phenotype includes intellectual disability, autism spectrum, epilepsy and sleep and behavior disturbances. Approximately 70 variants have been reported in the ALDH5A1 gene, half of them being missense variants. In this study, 34 missense variants, of which 22 novel, were evaluated by in silico analyses using PolyPhen2 and SIFT prediction tools. Subsequently, the effect of these variants on SSADH activity was studied by transient overexpression in HEK293 cells. These studies showed severe enzymatic activity impairment for 27 out of 34 alleles, normal activity for one allele and a broad range of residual activities (25 to 74%) for six alleles. To better evaluate the alleles that showed residual activity above 25%, we generated an SSADH-deficient HEK293-Flp-In cell line using CRISPR-Cas9, in which these alleles were stably expressed. This model proved essential in the classification as deficient for one out of the seven studied alleles. For 8 out of 34 addressed alleles, there were discrepant results among the used prediction tools, and/or in correlating the results of the prediction tools with the functional data. In case of diagnostic urgency of missense alleles, we propose the use of the transient transfection model for confirmation of their effect on the SSADH catalytic function, since this model resulted in fast and robust functional characterization for the majority of the tested variants. In selected cases, stable transfections can be considered and may prove valuable.

Elevated acute phase proteins affect pharmacokinetics in COVID-19 trials: Lessons from the CounterCOVID - imatinib study.

This study aimed to determine whether published pharmacokinetic (PK) models can adequately predict the PK profile of imatinib in a new indication, such as coronavirus disease 2019 (COVID-19). Total (bound + unbound) and unbound imatinib plasma concentrations obtained from 134 patients with COVID-19 participating in the CounterCovid study and from an historical dataset of 20 patients with gastrointestinal stromal tumor (GIST) and 85 patients with chronic myeloid leukemia (CML) were compared. Total imatinib area under the concentration time curve (AUC), maximum concentration (Cmax ) and trough concentration (Ctrough ) were 2.32-fold (95% confidence interval [CI] 1.34-3.29), 2.31-fold (95% CI 1.33-3.29), and 2.32-fold (95% CI 1.11-3.53) lower, respectively, for patients with CML/GIST compared with patients with COVID-19, whereas unbound concentrations were comparable among groups. Inclusion of alpha1-acid glycoprotein (AAG) concentrations measured in patients with COVID-19 into a previously published model developed to predict free imatinib concentrations in patients with GIST using total imatinib and plasma AAG concentration measurements (AAG-PK-Model) gave an estimated mean (SD) prediction error (PE) of -20% (31%) for total and -7.0% (56%) for unbound concentrations. Further covariate modeling with this combined dataset showed that in addition to AAG; age, bodyweight, albumin, CRP, and intensive care unit admission were predictive of total imatinib oral clearance. In conclusion, high total and unaltered unbound concentrations of imatinib in COVID-19 compared to CML/GIST were a result of variability in acute phase proteins. This is a textbook example of how failure to take into account differences in plasma protein binding and the unbound fraction when interpreting PK of highly protein bound drugs, such as imatinib, could lead to selection of a dose with suboptimal efficacy in patients with COVID-19.