Prediction of Toluene/Water Partition Coefficient in the SAMPL9 Blind Challenge: Assessment of Machine Learning and IEF-PCM/MST Continuum Solvation Models

In recent years the use of partition systems other than the widely used biphasic n-octanol/water has received increased attention to gain insight into the molecular features that dictate the lipophilicity of compounds. Thus, the difference between n-octanol/water and toluene/water partition coefficients has proven to be a valuable descriptor to study the propensity of molecules to form intramolecular hydrogen bonds and exhibit chameleon-like properties that modulate solubility and permeability. In this context, this study reports the experimental toluene/water partition coefficients (logPtol/w) for a series of 16 drugs that were selected as an external test set in the framework of the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) blind challenge. This external set has been used by the computational community to calibrate their methods in the current edition (SAMPL9) of this contest. Furthermore, the study also investigates the performance of two computational strategies for the prediction of logPtol/w. The first relies on the development of two machine learning (ML) models, which are built up by combining the selection of 11 molecular descriptors in conjunction with either multiple linear regression (MLR) and random forest regression (RFR) models to target a dataset of 252 experimental logPtol/w values. The second consists of the parametrization of the IEF-PCM/MST continuum solvation model from B3LYP/6-31G(d) calculations to predict the solvation free energies of 163 compounds in toluene and benzene. The performance of the ML and IEF-PCM/MST models has been calibrated against external test sets, including the compounds that define the SAMPL9 logPtol/w challenge. The results are used to discuss the merits and weaknesses of the two computational approaches

Prediction of toluene/water partition coefficients in the SAMPL9 blind challenge: assessment of machine learning and IEF-PCM/MST continuum solvation models

In recent years the use of partition systems other than the widely used biphasic n-octanol/water has received increased attention to gain insight into the molecular features that dictate the lipophilicity of compounds. Thus, the difference between n-octanol/water and toluene/water partition coefficients has proven to be a valuable descriptor to study the propensity of molecules to form intramolecular hydrogen bonds and exhibit chameleon-like properties that modulate solubility and permeability. In this context, this study reports the experimental toluene/water partition coefficients (log Ptol/w) for a series of 16 drugs that were selected as an external test set in the framework of the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) blind challenge. This external set has been used by the computational community to calibrate their methods in the current edition (SAMPL9) of this contest. Furthermore, the study also investigates the performance of two computational strategies for the prediction of log Ptol/w. The first relies on the development of two machine learning (ML) models, which are built up by combining the selection of 11 molecular descriptors in conjunction with either the multiple linear regression (MLR) or the random forest regression (RFR) model to target a dataset of 252 experimental log Ptol/w values. The second consists of the parametrization of the IEF-PCM/MST continuum solvation model from B3LYP/6-31G(d) calculations to predict the solvation free energies of 163 compounds in toluene and benzene. The performance of the ML and IEF-PCM/MST models has been calibrated against external test sets, including the compounds that define the SAMPL9 log Ptol/w challenge. The results are used to discuss the merits and weaknesses of the two computational approaches

COVID-19 in hospitalized HIV-positive and HIV-negative patients : A matched study

CatedresObjectives: We compared the characteristics and clinical outcomes of hospitalized individuals with COVID-19 with [people with HIV (PWH)] and without (non-PWH) HIV co-infection in Spain during the first wave of the pandemic. Methods: This was a retrospective matched cohort study. People with HIV were identified by reviewing clinical records and laboratory registries of 10 922 patients in active-follow-up within the Spanish HIV Research Network (CoRIS) up to 30 June 2020. Each hospitalized PWH was matched with five non-PWH of the same age and sex randomly selected from COVID-19@Spain, a multicentre cohort of 4035 patients hospitalized with confirmed COVID-19. The main outcome was all-cause in-hospital mortality. Results: Forty-five PWH with PCR-confirmed COVID-19 were identified in CoRIS, 21 of whom were hospitalized. A total of 105 age/sex-matched controls were selected from the COVID-19@Spain cohort. The median age in both groups was 53 (Q1-Q3, 46-56) years, and 90.5% were men. In PWH, 19.1% were injecting drug users, 95.2% were on antiretroviral therapy, 94.4% had HIV-RNA < 50 copies/mL, and the median (Q1-Q3) CD4 count was 595 (349-798) cells/μL. No statistically significant differences were found between PWH and non-PWH in number of comorbidities, presenting signs and symptoms, laboratory parameters, radiology findings and severity scores on admission. Corticosteroids were administered to 33.3% and 27.4% of PWH and non-PWH, respectively (P = 0.580). Deaths during admission were documented in two (9.5%) PWH and 12 (11.4%) non-PWH (P = 0.800). Conclusions: Our findings suggest that well-controlled HIV infection does not modify the clinical presentation or worsen clinical outcomes of COVID-19 hospitalization