RespiraTox - Development of a QSAR model to predict human respiratory irritants

Establishing short-term occupational exposure limits (STELs) for sensory irritants using predictive and in silico respiratory rate depression (RD50) models

Sensory irritation is a health endpoint that serves as the critical effect basis for many occupational exposure limits (OELs). Schaper 1993 described a significant relationship with high correlation between the measured exposure concentration producing a 50% respiratory rate decrease (RD50) in a standard rodent assay and the American Conference of Governmental Industrial Hygienists (ACGIH®) Threshold Limit Values (TLVs®) as time-weighted averages (TWAs) for airborne chemical irritants. The results demonstrated the potential use of the RD50 values for deriving full-shift TWA OELs protective of irritant responses. However, there remains a need to develop a similar predictive model for deriving workplace short-term exposure limits (STELs) for sensory irritants. The aim of our study was to establish a model capable of correlating the relationship between RD50 values and published STELs to prospectively derive short-term exposure OELs for sensory irritants. A National Toxicology Program (NTP) database that included chemicals with both an RD50 and established STELs was used to fit several linear regression models. A strong correlation between RD50s and STELs was identified, with a predictive equation of ln (STEL) (ppm) = 0.86 * ln (RD50) (ppm) - 2.42 and an R2 value of 0.75. This model supports the use of RD50s to derive STELs for chemicals without existing exposure recommendations. Further, for data-poor sensory irritants, predicted RD50 values from in silico quantitative structure activity relationship (QSAR) models can be used to derive STELs. Hence, in silico methods and statistical modeling can present a path forward for establishing reliable OELs and improving worker safety and health.

Update on irritant-induced occupational asthma

PURPOSE OF REVIEW

In this narrative review, we aim to highlight novel research findings on both acute/subacute irritant-induced asthma (IIA) and chronic exposure IIA (also called 'low dose' IIA).

RECENT FINDINGS

Novel case series showed that acute and subacute IIA cases had similar causal agents (e.g., acid or base aerosols/fumes, dusts, mixtures) but had occurred in different circumstances (accidents vs. regular work). Acute and subacute IIA cases had similar clinical characteristics but poorer short-term outcomes than sensitizer-induced occupational asthma patients. Novel large epidemiological studies reported associations between chronic occupational exposure to irritants and current adult-onset asthma and poor asthma control, and with a specific asthma endotype characterized by neutrophilic inflammation and oxidative stress. Recent studies reconfirmed the association of the use of disinfectants and cleaning products (especially sprays) with IIA. A role for genetic susceptibility has been suggested.

SUMMARY

Recent literature provided further understanding of both acute/subacute and chronic exposure IIA, in terms of causes, possible mechanisms, and consequences such as poor asthma control. Research is needed to clarify several aspects of IIA, including its frequency (still likely underestimated), modulating factors, and mechanisms. Research aiming at improving irritant exposure assessment, including intensity/duration, and determining relevant exposure windows would be welcome.

Cyclohexane-1,3-dione Derivatives as Future Therapeutic Agents for NSCLC: QSAR Modeling, In Silico ADME-Tox Properties, and Structure-Based Drug Designing Approach

The abnormal expression of the c-Met tyrosine kinase has been linked to the proliferation of several human cancer cell lines, including non-small-cell lung cancer (NSCLC). In this context, the identification of new c-Met inhibitors based on heterocyclic small molecules could pave the way for the development of a new cancer therapeutic pathway. Using multiple linear regression (MLR)-quantitative structure-activity relationship (QSAR) and artificial neural network (ANN)-QSAR modeling techniques, we look at the quantitative relationship between the biological inhibitory activity of 40 small molecules derived from cyclohexane-1,3-dione and their topological, physicochemical, and electronic properties against NSCLC cells. In this regard, screening methods based on QSAR modeling with density-functional theory (DFT) computations, in silico pharmacokinetic/pharmacodynamic (ADME-Tox) modeling, and molecular docking with molecular electrostatic potential (MEP) and molecular mechanics-generalized Born surface area (MM-GBSA) computations were used. Using physicochemical (stretch-bend, hydrogen bond acceptor, Connolly molecular area, polar surface area, total connectivity) and electronic (total energy, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels) molecular descriptors, compound 6d is identified as the optimal scaffold for drug design based on in silico screening tests. The computer-aided modeling developed in this study allowed us to design, optimize, and screen a new class of 36 small molecules based on cyclohexane-1,3-dione as potential c-Met inhibitors against NSCLC cell growth. The in silico rational drug design approach used in this study led to the identification of nine lead compounds for NSCLC therapy via c-Met protein targeting. Finally, the findings are validated using a 100 ns series of molecular dynamics simulations in an aqueous environment on c-Met free and complexed with samples of the proposed lead compounds and Foretinib drug.