Prediction of Human Lethal Doses and Concentrations of MEIC Chemicals from Rodent LD Values: An Attempt to Make Some Reparation.

The prediction of human toxicities from animal toxicity tests is often poor, and is now discouraged and in some cases banned, especially those involving the LD test. However, there is a vast number of historical LD data in both public and in-house repositories that are being put to little use. This study examined the correlations between human lethality (doses and concentrations) of 36 MEIC chemicals and the median values of a large number of mouse and rat LD values obtained for four different routes of administration.

Provider responses to a global budgeting system: The case of drug expenditures in Taiwan hospitals.

In July 2002, a global budgeting system was imposed on hospitals in Taiwan. This system set a fixed budget for all hospitals within a region but included special provisions that sheltered reimbursements for drug expenditures. We study the size and nature of changes in hospital physicians' use of drugs for outpatient care following this budgetary change and find that drug expenditures for outpatient care increased by 11.

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited.

Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19.

Design, Setting, And Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin.

Effect of the structural factors of organic compounds on the acute toxicity toward .

The acute toxicity of organic compounds towards was subjected to QSAR analysis. The two-dimensional simplex representation of molecular structure (2D SiRMS) and the support vector machine (SVM), gradient boosting (GBM) methods were used to develop QSAR models. Adequate regression QSAR models were developed for incubation of 24 h.

Sofosbuvir/velpatasvir for 12 weeks in hepatitis C virus-infected patients with end-stage renal disease undergoing dialysis.

Background & Aims: Although off-label use of sofosbuvir-containing regimens occurs regularly in patients with hepatitis C virus (HCV) infection undergoing dialysis for severe renal impairment or end-stage renal disease (ESRD), these regimens are not licensed for this indication, and there is an absence of dosing recommendations in this population. This study evaluated the safety and efficacy of sofosbuvir/velpatasvir in patients with HCV infection with ESRD undergoing dialysis.

Methods: In this phase II, single-arm study, 59 patients with genotype 1-6 HCV infection with ESRD undergoing hemodialysis or peritoneal dialysis received open-label sofosbuvir/velpatasvir (400 mg/100 mg) once daily for 12 weeks.

Contribution assessment of multiparameter optimization descriptors in CNS penetration.

Assessment of the influence of six physicochemical properties used in the multiparameter optimization (MPO) approach for chemical penetration of the blood-brain barrier was carried out by means of application of logistic regression and multiple linear regression, using a data set of 578 diverse chemicals. It was found that use of an aggregation MPO-score descriptor did not give satisfactory results with central nervous system (CNS)/non-CNS classification. Thus an application of the MPO approach for CNS penetration is ambiguous.

Classification (Agonist/Antagonist) and Regression "Structure-Activity" Models of Drug Interaction with 5-HT6.

Introduction: One promising target for novel psychotropic drugs is the 5-HT6 receptor, GProtein- Coupled Receptor (GPCR) family, displaying seven transmembrane domains. There is considerable interest in how both 5-HT6 receptor agonist and antagonist compounds can have marked procognitive effects.

Methods: An exact structure of the 5-HT6 receptor is not available, so application of powerful methods of (Q)SAR and molecular modelling, which play an essential role in modern drug design, are currently limited to structure-based homology models.

Aqueous Drug Solubility: What Do We Measure, Calculate and QSPR Predict?

Detailed critical analysis of publications devoted to QSPR of aqueous solubility is presented in the review with discussion of four types of aqueous solubility (three different thermodynamic solubilities with unknown solute structure, intrinsic solubility, solubility in physiological media at pH=7.4 and kinetic solubility), variety of molecular descriptors (from topological to quantum chemical), traditional statistical and machine learning methods as well as original QSPR models.

Simulation-based Assessment to Reliably Identify Key Resident Performance Attributes.

Background: Obtaining reliable and valid information on resident performance is critical to patient safety and training program improvement. The goals were to characterize important anesthesia resident performance gaps that are not typically evaluated, and to further validate scores from a multiscenario simulation-based assessment.

Methods: Seven high-fidelity scenarios reflecting core anesthesiology skills were administered to 51 first-year residents (CA-1s) and 16 third-year residents (CA-3s) from three residency programs.

Six global and local QSPR models of aqueous solubility at pH = 7.4 based on structural similarity and physicochemical descriptors.

Aqueous solubility at pH = 7.4 is a very important property for medicinal chemists because this is the pH value of physiological media. The present work describes the application of three different methods (support vector machine (SVM), random forest (RF) and multiple linear regression (MLR)) and three local quantitative structure-property relationship (QSPR) models (regression corrected by nearest neighbours (RCNN), arithmetic mean property (AMP) and local regression property (LoReP)) to construct stable QSPRs with clear mechanistic interpretation.

Improved correlation between animal and human potency of non-steroidal anti-inflammatory drugs using quantitative structure-activity relationships (QSARs).

Animal models are known not to predict human responses well, in general. However, we have been able to demonstrate that, for a series of non-steroidal anti-inflammatory drugs that are or were in clinical use, the incorporation of two simple physicochemical properties results in excellent correlations between human and rodent potencies for anti-inflammatory, analgesic and anti-pyretic activities. This has the potential to allow the use of historical data to improve drug development.

Individualized remediation during fellowship training.

The Accreditation Council for Graduate Medical Education requires medical training programs to monitor, track, and formally document a fellow's performance. If deficiencies are found, programs are expected to prepare and implement an effective plan of action for improvement and to ensure that graduates acquire the personal and professional attributes of an independent physician. We revised our evaluation policy and instituted a remediation protocol in 2008.

Safety and cost-effectiveness of port removal outside of the operating room among pediatric patients.

Purpose: The current emphasis on fiscally responsible health spending in the era of the Affordable Care Act and other health care reform necessitates cost-conscious delivery of care. "Value" in health care is defined as the quality of care divided by the cost. As such, health systems optimize value by providing the most cost-effective care possible without sacrificing safety or outcomes.

Physicochemical property profile for brain permeability: comparative study by different approaches.

A comparative study of classification models of brain penetration by different approaches was carried out on a training set of 1000 chemicals and drugs, and an external test set of 100 drugs. Ten approaches were applied in this work: seven medicinal chemistry approaches (including "rule of 5" and multiparameter optimization) and also three SAR techniques: logistic regression (LR), random forest (RF) and support vector machine (SVM). Forty-one different medicinal chemistry descriptors representing diverse physicochemical properties were used in this work.

Mechanism-Based QSAR Modeling of Skin Sensitization.

Many chemicals can induce skin sensitization, and there is a pressing need for non-animal methods to give a quantitative indication of potency. Using two large published data sets of skin sensitizers, we have allocated each sensitizing chemical to one of 10 mechanistic categories and then developed good QSAR models for the seven categories that have a sufficient number of chemicals to allow modeling. Both internal and external validation checks showed that each model had good predictivity.

Order of Magnitude Markers: An Empirical Study on Large Magnitude Number Detection.

In this paper we introduce Order of Magnitude Markers (OOMMs) as a new technique for number representation. The motivation for this work is that many data sets require the depiction and comparison of numbers that have varying orders of magnitude. Existing techniques for representation use bar charts, plots and colour on linear or logarithmic scales.

QSAR study of some anti-hyperglycaemic sulphonylurea drugs.

Sulphonylureas are widely used anti-hyperglycaemic drugs for the treatment of type 2 diabetes. The only published quantitative structure-activity relationship (QSAR) models for sulphonylurea drugs have been found to be questionable, for a number of reasons. We have re-analysed the human anti-hyperglycaemic potencies, acute mouse intraperitoneal toxicities (LD50) and plasma protein-binding abilities of the 15 drugs using multiple linear regression and obtained good QSAR models for each endpoint.

In silico Prediction of Aqueous Solubility: a Comparative Study of Local and Global Predictive Models.

32 Quantitative Structure-Property Relationship (QSPR) models were constructed for prediction of aqueous intrinsic solubility of liquid and crystalline chemicals. Data sets contained 1022 liquid and 2615 crystalline compounds. Multiple Linear Regression (MLR), Support Vector Machine (SVM) and Random Forest (RF) methods were used to construct global models, and k-nearest neighbour (kNN), Arithmetic Mean Property (AMP) and Local Regression Property (LoReP) were used to construct local models.

Use of artificial neural networks in the QSAR prediction of physicochemical properties and toxicities for REACH legislation.

With the introduction of the REACH legislation in the European Union, there is a requirement for property and toxicity data on chemicals produced in or imported into the EU at levels of 1 tonne/year or more. This has meant an increase in the in silico prediction of such data. One of the chief predictive approaches is QSAR (quantitative structure-activity relationships), which is widely used in many fields.

Calculation of aqueous solubility of crystalline un-ionized organic chemicals and drugs based on structural similarity and physicochemical descriptors.

Solubilities of crystalline organic compounds calculated according to AMP (arithmetic mean property) and LoReP (local one-parameter regression) models based on structural and physicochemical similarities are presented. We used data on water solubility of 2615 compounds in un-ionized form measured at 25±5 °C. The calculation results were compared with the equation based on the experimental data for lipophilicity and melting point.

QSAR modeling: where have you been? Where are you going to?

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling.

Extended duration therapy with pegylated interferon and ribavirin for patients with genotype 3 hepatitis C and advanced fibrosis: final results from the STEPS trial.

Background & Aims: Patients with genotype 3 hepatitis C virus (HCV) infection and cirrhosis have poor response rates after 24 weeks treatment with pegylated interferon and ribavirin. Treatment for 48 weeks is therefore recommended, although the benefits of this are untested. We examined extended therapy in patients with genotype 3 HCV and advanced fibrosis.

Accommodating to motor difficulties and communication impairments in people with autism: the MORE intervention model.

Motor impairment in individuals with autism potentially impacts on their development in all spheres. This paper is particularly concerned with people with severe communication impairments suggesting that recognition of the impact of motor impairments on their lives could lead to more effective interventions being developed. One such intervention is the MORE (Means, Opportunities, Reasons, and Expectations) model, founded on the "least dangerous assumption," that is assuming competence until otherwise established through long-term observation and assessment.

QSPR prediction of physico-chemical properties for REACH.

For registration of a chemical, European Union REACH legislation requires information on the relevant physico-chemical properties of the chemical. Predicted property values can be used when the predictions can be shown to be valid and adequate. The relevant physico-chemical properties that are amenable to prediction are: melting/freezing point, boiling point, relative density, vapour pressure, surface tension, water solubility, n-octanol-water partition coefficient, flash point, flammability, explosive properties, self-ignition temperature, adsorption/desorption, dissociation constant, viscosity, and air-water partition coefficient (Henry's law constant).