Which Compound to Select in Lead Optimization? Prospectively Validated Proteochemometric Models Guide Preclinical Development

In quite a few diseases, drug resistance due to target variability poses a serious problem in pharmacotherapy. This is certainly true for HIV, and hence, it is often unknown which drug is best to use or to develop against an individual HIV strain. In this work we applied ‘proteochemometric’ modeling of HIV Non-Nucleoside Reverse Transcriptase (NNRTI) inhibitors to support preclinical development by predicting compound performance on multiple mutants in the lead selection stage. Proteochemometric models are based on both small molecule and target properties and can thus capture multi-target activity relationships simultaneously, the targets in this case being a set of 14 HIV Reverse Transcriptase (RT) mutants. We validated our model by experimentally confirming model predictions for 317 untested compound – mutant pairs, with a prediction error comparable with assay variability (RMSE 0.62). Furthermore, dependent on the similarity of a new mutant to the training set, we could predict with high accuracy which compound will be most effective on a sequence with a previously unknown genotype. Hence, our models allow the evaluation of compound performance on untested sequences and the selection of the most promising leads for further preclinical research. The modeling concept is likely to be applicable also to other target families with genetic variability like other viruses or bacteria, or with similar orthologs like GPCRs

Assessment of portal hypertension severity using machine learning models in patients with compensated cirrhosis

Background & Aims: In individuals with compensated advanced chronic liver disease (cACLD), the severity of portal hypertension (PH) determines the risk of decompensation. Invasive measurement of the hepatic venous pressure gradient (HVPG) is the diagnostic gold standard for PH. We evaluated the utility of machine learning models (MLMs) based on standard laboratory parameters to predict the severity of PH in individuals with cACLD. Methods: A detailed laboratory workup of individuals with cACLD recruited from the Vienna cohort (NCT03267615) was utilised to predict clinically significant portal hypertension (CSPH, i.e., HVPG ≥10 mmHg) and severe PH (i.e., HVPG ≥16 mmHg). The MLMs were then evaluated in individual external datasets and optimised in the merged cohort. Results: Among 1,232 participants with cACLD, the prevalence of CSPH/severe PH was similar in the Vienna (n = 163, 67.4%/35.0%) and validation (n = 1,069, 70.3%/34.7%) cohorts. The MLMs were based on 3 (3P: platelet count, bilirubin, international normalised ratio) or 5 (5P: +cholinesterase, +gamma-glutamyl transferase, +activated partial thromboplastin time replacing international normalised ratio) laboratory parameters. The MLMs performed robustly in the Vienna cohort. 5P-MLM had the best AUCs for CSPH (0.813) and severe PH (0.887) and compared favourably to liver stiffness measurement (AUC: 0.808). Their performance in external validation datasets was heterogeneous (AUCs: 0.589-0.887). Training on the merged cohort optimised model performance for CSPH (AUCs for 3P and 5P: 0.775 and 0.789, respectively) and severe PH (0.737 and 0.828, respectively). Conclusions: Internally trained MLMs reliably predicted PH severity in the Vienna cACLD cohort but exhibited heterogeneous results on external validation. The proposed 3P/5P online tool can reliably identify individuals with CSPH or severe PH, who are thus at risk of hepatic decompensation. Impact and implications: We used machine learning models based on widely available laboratory parameters to develop a non-invasive model to predict the severity of portal hypertension in individuals with compensated cirrhosis, who currently require invasive measurement of hepatic venous pressure gradient. We validated our findings in a large multicentre cohort of individuals with advanced chronic liver disease (cACLD) of any cause. Finally, we provide a readily available online calculator, based on 3 (platelet count, bilirubin, international normalised ratio) or 5 (platelet count, bilirubin, activated partial thromboplastin time, gamma-glutamyltransferase, choline-esterase) widely available laboratory parameters, that clinicians can use to predict the likelihood of their patients with cACLD having clinically significant or severe portal hypertension

The Belgian Association for Study of the Liver guidance document on the management of non-alcoholic fatty liver disease

Non-Alcoholic Fatty Liver Disease (NAFLD) is highly prevalent and associated with considerable liver-related and non-liver-related morbidity and mortality. There is, however, a lot of uncertainty on how to handle NAFLD in clinical practice. The current guidance document, compiled under the aegis of the Belgian Association for the Study of the Liver by a panel of experts in NAFLD, from a broad range of different specialties, covers many questions encountered in daily clinical practice regarding diagnosis, screening, therapy and follow-up. Guidance statements in this document are based on the available evidence whenever possible. In case of absence of evidence or inconsistency of the data, guidance statements were formulated based on consensus of the expert panel. This guidance document is intended as a help for clinicians (general practitioners and all involved specialties) to implement the most recent evidence and insights in the field of NAFLD within a Belgian perspective

Decompensation in Advanced Nonalcoholic Fatty Liver Disease May Occur at Lower Hepatic Venous Pressure Gradient Levels Than in Patients With Viral Disease

Background & Aims: Portal hypertension is the strongest predictor of hepatic decompensation and death in patients with cirrhosis. However, its discriminatory accuracy in patients with nonalcoholic fatty liver disease (NAFLD) has been challenged because hepatic vein catheterization may not reflect the real portal vein pressure as accurately as in patients with other etiologies. We aimed to evaluate the relationship between hepatic venous pressure gradient (HVPG) and presence of portal hypertension–related decompensation in patients with advanced NAFLD (aNAFLD). Methods: Multicenter cross-sectional study included 548 patients with aNAFLD and 444 with advanced RNA-positive hepatitis C (aHCV) who had detailed portal hypertension evaluation (HVPG measurement, gastroscopy, and abdominal imaging). We examined the relationship between etiology, HVPG, and decompensation by logistic regression models. We also compared the proportions of compensated/decompensated patients at different HVPG levels. Results: Both cohorts, aNAFLD and aHVC, had similar baseline age, gender, Child-Pugh score, and Model for End-Stage Liver Disease score. Median HVPG was lower in the aNAFLD cohort (13 vs 15 mmHg) despite similar liver function and higher rates of decompensation in aNAFLD group (32% vs 25%; P =.019) than in the aHCV group. For any of the HVPG cutoff analyzed (<10, 10–12, or 12 mmHg) the prevalence of decompensation was higher in the aNAFLD group than in the aHCV group. Conclusions: Patients with aNAFLD have higher prevalence of portal hypertension–related decompensation at any value of HVPG as compared with aHCV patients. Longitudinal studies aiming to identify HVPG thresholds able to predict decompensation and long-term outcomes in aNAFLD population are strongly needed