VirtualToxLab – in silico Prediction of the Endocrine-Disrupting Potential of Drugs and Chemicals

In the last decade, we have developed and validated an in silico concept based on multidimensional QSAR (mQSAR) for the prediction of the toxic potential of drugs and environmental chemicals. Presently, the VirtualToxLab includes eleven so-called virtual test kits for estrogen (?/?), androgen, thyroid (?/?), glucocorticoid, aryl hydrocarbon, mineralocorticoid and peroxisome proliferator-activated receptor ? as well as for the enzymes cytochrome P450 3A4 and 2A13. The surrogates have been tested against a total of 824 compounds and are able to predict the binding affinity close to the experimental uncertainty with only six of the 194 test compounds giving calculated results more than a factor of 10 off the experimental binding affinity and the maximal individual deviation not exceeding a factor of 15. These results suggest that our approach is suited for the in silico identification of endocrine-disrupting effects triggered by drugs and environmental chemicals. Most recently, the technology has been made available through the Internet for academic laboratories, hospitals and environmental organizations

Mixed-model QSAR at the glucocorticoid and liver X receptors

The presence of hormonally active compounds in the biosphere has become a worldwide environmental concern, and measures such as policy acts and regulations try to address the problem, both in Europe and in the United States. Such compounds, referred to as endocrine disruptors, may alter the functions of the endocrine system and consequently cause adverse health effects in organism, or its progeny, or populations.1 A safe in silico identification of the toxic potential of drugs and chemicals is therefore highly desirable by both regulatory bodies, and the pharmaceutical industry. Nuclear receptors regulate biological functions such as cell growth and differentiation, metabolic processes, reproduction and development, intracellular signaling and can be involved in carcinogenesis through control of gene expression.2 Chemicals that disrupt the endocrine system interfere with the function of nuclear receptors, alter their functions and consequently cause adverse health effects.1 In this thesis, the development and validation of in silico three-dimensional models for the glucocorticoid and the liver X receptors, both belonging to the nuclear receptor superfamily, are presented. These models aim at the screening of drug candidates for glucocorticoid and liver X activity and of environmental chemicals for potential endocrine-disrupting activity. Different in silico-based tools and protocols were used to model receptor-ligand interactions. Molecular dynamics simulations enabled to gain an insight into the dynamical character of the protein-ligand interactions. An appropriate consideration of receptor flexibility (induced fit) was a prerequisite for the identification of realistic binding modes, which was performed with flexible docking. Once a suitable alignment was obtained, QSAR models were built, using two different technologies, and tested by the application to external validation sets, scramble tests and consensus scoring. The models have been added to the VirtualToxLab™3, 4 – a technology for the in silico identification of the toxic (endocrine-disrupting) potential of drugs and environmental chemicals. Special consideration was given to the role of hydrophobic effect in ligand binding. An empirical scoring function (Heidi: Hydrophobic Effect in Drug Interactions) was developed to quantify the hydrophobic effect for scoring protein–ligand binding energies. The use of HEidi, together with electrostatic, van der Waals and hydrogen bond energies, in the ranking of docking poses provided encouraging results when applied to glucocorticoid and liver X receptor complexes, but for a generalized statement more extensive evaluations are needed

In silico prediction of brain exposure: drug free fraction, unbound brain to plasma concentration ratio and equilibrium half-life.

The focus of CNS drug pharmacokinetics programs has recently shifted from determining the total concentrations in brain and blood to considering also unbound fractions and concentrations. Unfortunately, assessing unbound brain exposure experimentally requires demanding in vivo and in vitro studies. We propose a physical model, based on lipid binding and pH partitioning, to predict in silico the unbound volume of distribution in the brain. The model takes into account the partition of a drug into lipids, interstitial fluid and intracellular compartments of the brain. The results are in good agreement with the experimental data, suggesting that the contributions of lipid binding and pH partitioning are important in determining drug exposure in brain. The predicted values are used, together with predictions for plasma protein binding, as corrective terms in a second model to derive the unbound brain to plasma concentration ratio starting from experimental values of total concentration ratio. The calculated values of brain free fraction and passive permeability are also used to qualitatively determine the brain to plasma equilibration time in a model that shows promising results but is limited to a very small set of compounds. The models we propose are a step forward in understanding and predicting pharmacologically relevant exposure in brain starting from compounds' chemical structure and neuropharmacokinetics, by using experimental total brain to plasma ratios, in silico calculated properties and simple physics-based approaches. The models can be used in central nervous system drug discovery programs for a fast and cheap assessment of unbound brain exposure. For existing compounds, the unbound ratios can be derived from experimental values of total brain to plasma ratios. For both existing and hypothetical compounds, the unbound volume of distribution due to lipid binding and pH partitioning can be calculated starting only from the chemical structure

Blood cell differential count discretisation modelling to predict survival in adults reporting to the emergency room: a retrospective cohort study

OBJECTIVES: To assess the survival predictivity of baseline blood cell differential count (BCDC), discretised according to two different methods, in adults visiting an emergency room (ER) for illness or trauma over 1 year. DESIGN: Retrospective cohort study of hospital records. SETTING: Tertiary care public hospital in northern Italy. PARTICIPANTS: 11 052 patients aged >18 years, consecutively admitted to the ER in 1 year, and for whom BCDC collection was indicated by ER medical staff at first presentation. PRIMARY OUTCOME: Survival was the referral outcome for explorative model development. Automated BCDC analysis at baseline assessed haemoglobin, mean cell volume (MCV), red cell distribution width (RDW), platelet distribution width (PDW), platelet haematocrit (PCT), absolute red blood cells, white blood cells, neutrophils, lymphocytes, monocytes, eosinophils, basophils and platelets. Discretisation cut-offs were defined by benchmark and tailored methods. Benchmark cut-offs were stated based on laboratory reference values (Clinical and Laboratory Standards Institute). Tailored cut-offs for linear, sigmoid-shaped and U-shaped distributed variables were discretised by maximally selected rank statistics and by optimal-equal HR, respectively. Explanatory variables (age, gender, ER admission during SARS-CoV2 surges and in-hospital admission) were analysed using Cox multivariable regression. Receiver operating curves were drawn by summing the Cox-significant variables for each method. RESULTS: Of 11 052 patients (median age 67 years, IQR 51-81, 48% female), 59% (n=6489) were discharged and 41% (n=4563) were admitted to the hospital. After a 306-day median follow-up (IQR 208-417 days), 9455 (86%) patients were alive and 1597 (14%) deceased. Increased HRs were associated with age >73 years (HR=4.6, 95% CI=4.0 to 5.2), in-hospital admission (HR=2.2, 95% CI=1.9 to 2.4), ER admission during SARS-CoV2 surges (Wave I: HR=1.7, 95% CI=1.5 to 1.9; Wave II: HR=1.2, 95% CI=1.0 to 1.3). Gender, haemoglobin, MCV, RDW, PDW, neutrophils, lymphocytes and eosinophil counts were significant overall. Benchmark-BCDC model included basophils and platelet count (area under the ROC (AUROC) 0.74). Tailored-BCDC model included monocyte counts and PCT (AUROC 0.79). CONCLUSIONS: Baseline discretised BCDC provides meaningful insight regarding ER patients' survival

Blood cell differential count discretization modeling predicts survival in adults reporting to the emergency room: a retrospective cohort study

Objectives: to assess survival predictivity of baseline blood cell differential count (BCDC), discretized according to two different methods, in adults visiting the Emergency Room (ER) for illness or trauma over one-year. Design: Retrospective cohort study of hospital records. Setting: Tertiary care public hospital in northern Italy. Participants: 11052 patients aged > 18 years, consecutively admitted to the ER in one year, and for whom BCDC collection was indicated by ER medical staff at first presentation. Primary outcome: Survival was the referral outcome for explorative model development. Automated BCDC analysis at baseline assessed hemoglobin, red cell mean volume (MCV) and distribution-width (RDW), platelet distribution-width (PDW), plateletcrit (PCT), absolute red blood cells, white blood cells, neutrophils, lymphocytes, monocytes, eosinophils, basophils, and platelets. Discretization cutoffs were defined by Benchmark and Tailored methods. Benchmark cutoffs were stated on laboratory reference values (CLSI). Tailored cutoffs for linear, sigmoid-shaped and for U-shaped distributed variables were discretized by Maximally Selected Rank Statistics and by Optimal-Equal Hazard Ratio respectively. Explanatory variables (age, gender, ER admission during SARS-CoV2 surges, in-hospital admission) were analyzed using Cox multivariable regression. ROC curves were drawn by sum of Cox-significant variables for each method. Results: Of 11052 patients (median age 67 years, IQR 51–81, 48% female), 59% (n=6489) were discharged and 41% (n=4563) were admitted in hospital. After a 306-day median follow up (IQR 208–417 days), 9455 (86%) patients were alive and 1597 (14%) deceased. Increased HRs were associated with age >73-years (HR=4.6 CI=4.0–5.2), in-hospital admission (HR=2.2 CI=1.9–2.4), ER admission during SARS-CoV2 surges (Wave-I HR=1.7 CI=1.5–1.9); Wave-II HR=1.2 CI=1.0–1.3). Gender, hemoglobin, MCV, RDW, PDW, neutrophils, lymphocytes and eosinophils counts were significant in overall. Benchmark-BCDC model included basophils and platelet count (AUROC 0.74). Tailored-BCDC model included monocyte counts and plateletcrit (AUROC 0.79). Conclusions: baseline discretized BCDC provides meaningful insight regarding Emergency Room patients survival.Complete blood cell differential count (BCDC) was performed using the automated Sysmex XN-9000 analyzer on peripheral blood samples taken at baseline and stored in hospital Lab electronic archives by dates (starting 2020-01-01 ending 2020-12-31) and by dept (Pronto Soccorso). Data were handled in CSV format by RStudio. Survival was the referral outcome for explorative model development and was assessed on June 30th, 2021, by a population registry office query through the NHS territorial service. Lab data were converted from tong to wide and dataframe was joined with the survival dataframe by unique personal alphanumeric code assigned by Italian authorities to each citizen. Being under category of sensitive information, although codified and not overt, personal alphanumeric codes were then deleted by assigning each patient a sequential coding number in dataframe. Duplicates were deleted. Predictors were searched among the BCDC first automated analysis assessment at presentation of hemoglobin (Hb), mean red cell volume (MCV), red cell distribution width (RDW), platelet distribution width (PDW), platelet hematocrit (piastrinocrit) (PCT) and absolute count of red blood cells (RBC), white blood cells (WBC), neutrophils (Neu), lymphocytes (Lym), monocytes (Mon), eosinophils (Eos), basophils (Bas), and platelets (PLT). Missing data were excluded, as only patients having BCDC records were evaluated. Analysis was performed by R studio and by Jamovi free R-based software (The jamovi project (2021). jamovi. [Computer Software]. Retrieved from https://www.jamovi.org). The "Benchmark" reference model was set by discretization of BCDC continuous values on our laboratory reference interval, established according to the C28-A3 guideline by the Clinical and Laboratory Standards Institute (CLSI). The "Tailored" discretization was set as follows. The relationship between each continuous variable and log relative hazard was plotted using the penalized B-splines (psplines) technique] for fitting the nonlinear effect of covariate in Cox models, by minimizing pitfalls associated with dichotomization of biological variables.Variables were treated differently according to their respective distribution profile. Linear and sigmoid-shaped variables were dichotomized by the maximally selected rank statistic method (MSRS). U-shaped variables were univariately discretized by cutoff point determination using the optimal-equal hazard ratio method (OEHR) (Chen Y, Huang J, He X, et al. BMC Med Res Methodol. 2019 May 9;19(1):96. doi: 10.1186/s12874-019-0738-4