Evaluation of the Possibility of Calculating LD50 and LD10 Using a Modified Script in the R Environment

Regulâtornye issledovaniâ i èkspertiza lekarstvennyh sredstv., Год журнала: 2025, Номер 15(2), С. 213 - 221

Опубликована: Май 1, 2025

INTRODUCTION . The median lethal dose (LD 50 ) and the low 10 are critical parameters for safety of medicinal products. Sometimes, pharmacopoeial probit method (PM) fails to calculate LD value, calculation result is obviously lower than true value. In such cases, use other computational techniques warranted. AIM. This study aimed evaluate potential a script in R environment as tool calculating medicines. MATERIALS AND METHODS compared results determining using spreadsheet-based PM modified (MS). lm() function (linear regression model) was used establish relationships between LD50 values obtained those calculated MS. RESULTS. A originally developed by S. Young supplemented simplify its use. modification reduced amount input data required calculation, added ability values, improved visual clarity results. Reducing step size seq() shown improve output smoothness when MS yielded jagged mortality curve. MS-derived were within confidence limits (P=0.95). analysis confirmed accuracy MS-based calculations, which demonstrated statistically insignificant systematic error, significant dependence at P=0.999, high coefficient determination ( 2 ). If underestimates analyst should be guided CONCLUSIONS. experimental demonstrate applicability testing some cases presented article, custom offers an advantage over current method. tentative direction further work may automation calculation.

Язык: Английский

---

Adverse Outcome Pathway and Machine Learning to Predict Drug Induced Seizure Liability

ACS Chemical Neuroscience, Год журнала: 2025, Номер unknown

Опубликована: Май 14, 2025

Central nervous system (CNS) drugs have the highest clinical attrition, often due to CNS-related toxicities such as drug-induced seizures (DIS). Early prediction of DIS risk could reduce failure rates and optimize drug development by prioritizing testing in experimental models DIS. Using seizure-relevant Adverse Outcome Pathways (AOPs) from various sources, we identified 67 seizure-associated protein targets. Biological activity data (EC50, IC50, Ki) for these targets were curated ChEMBL, enabling ∼2000 regression classification (random forest, support vector, XGBoost) models. Support vector (SVR) achieved an average MAE 0.54 ± 0.09 (-log M), while random forest classifiers yielded mean ROC AUC, accuracy, recall 0.88, 0.85, 0.70, respectively (5-fold CV) across all Multitarget XGBoost concatenating ECFP6 fingerprints target encodings (one-hot or ProtBERT) also demonstrated excellent overall performance, although their predictive accuracy was notably lower leave-out sets compared individual target-specific These used predict a seizure-liability set with target-annotated predictions. Overall, our findings utility using machine-learning aid early toxicity prioritization CNS attrition.

Язык: Английский

---

In silico ADME/tox comes of age: twenty years later

Xenobiotica, Год журнала: 2023, Номер 54(7), С. 352 - 358

Опубликована: Авг. 4, 2023

In the early 2000s pharmaceutical drug discovery was beginning to use computational approaches for absorption, distribution, metabolism, excretion and toxicity (ADME/Tox, also known as ADMET) prediction. This emphasis on prediction an effort reduce risk of later stage failures from ADME/Tox.Much has been written in intervening twenty plus years significant expenditure occurred companies developing these

Язык: Английский

---

Sequential Contrastive and Deep Learning Models to Identify Selective Butyrylcholinesterase Inhibitors

Journal of Chemical Information and Modeling, Год журнала: 2024, Номер 64(8), С. 3161 - 3172

Опубликована: Март 27, 2024

Butyrylcholinesterase (BChE) is a target of interest in late-stage Alzheimer's Disease (AD) where selective BChE inhibitors (BIs) may offer symptomatic treatment without the harsh side effects acetylcholinesterase (AChE) inhibitors. In this study, we explore multiple machine learning strategies to identify BIs

Язык: Английский

---

Evaluation of synergistic effect of entomopathogenic fungi Beauveria bassiana and Lecanicillium lecacii on the mosquito Culex quinquefaciatus

PLoS ONE, Год журнала: 2024, Номер 19(9), С. e0308707 - e0308707

Опубликована: Сен. 6, 2024

Vector-borne diseases resulted into several cases of human morbidity and mortality over the years among them is filariasis, caused by mosquito Culex quinquefasciatus . Developing novel strategies for control without jeopardizing environmental conditions has always been a topic discussion research. Integrated Vector Management (IVM) emphasizes comprehensive approach use range vector control. Recent research evaluated two entomopathogenic fungi; Beauveria bassiana Lecanicillium lecanii in IVM, which can serve as potential organic insecticide population However, their combined efficacy not yet against prior gap knowledge still existing. So, this was an attempt to bridge up (1) Assessing on (2) To investigate sub-lethal concentration (LC 50 ) fungal (3) examine post-mortem effects under Scanning Electron Microscope (SEM). The larval pathogenicity assay performed 4 th instar C larvae. Individual processed solution B L were procured test efficacy, solutions mixed equal proportions. evaluate ), different concentrations prepared serial dilations. recorded after 24 hours each concentration. Upon treatment evaluation, LC values 0.25 x 10 spores/ml 0.12 respectively 0.06 3 spores/ml. This clearly indicated that fungi more significant. Further, SEM analysis revealed morphological deformities extensive body perforations upon treatment. These findings suggested combining be effective way controlling

Язык: Английский

---

Studies on the Effect of Cadmium Chloride on the Behavioral and Histopathological Changes in Cyprinus carpio : A Short-Term Bioassay

Environment and Ecology, Год журнала: 2024, Номер 42(2B), С. 790 - 800

Опубликована: Июнь 1, 2024

Cadmium (Cd) is a common heavy metal known for its detrimental impact on aquatic organisms. The presence of this non-essential element in the food chain poses significant threat to human health due biomagnifying effects. present study was undertaken investigate effects cadmium toxicity behavioral and histopathological alterations gill, liver kidney tissues carp species, Cyprinus carpio. Six groups experimental fish with three replicates were exposed different concentrations chloride i.e., 0, 60, 70, 80, 90 100 mg/L respectively period 96 h. h LC50 value C. carpio determined be 74.65 mg/L. Treated fishes higher doses exhibited increased breathing, accelerated ventilation rapid opercular movement air gulping, erratic swimming, collision against wall, loss equilibrium, jumping, restlessness sluggishness. Histopathological changes also observed tissue. gills marked by lamellar fusion, epithelial hyperplasia, lifting, telangiectasia, aneurism, blood congestion necrosis cells. trunk glomerular distortion, fibrous edema, infiltration edematous fluid, expansion Bowman’s space, hemorrhage, damage uriniferous tubules. hepatocytes showed cytoplasmic vacuolation, pyknotic nucleus, hypertrophy hepatocytes, erythrocyte infiltration, patchy degeneration, enlargement sinusoids loosening hepatic tissues. findings demonstrated that acute exposure has essential organs normal behavior, potentially leading harmful consequences populations.

Язык: Английский

---

Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery

Journal of Chemical Information and Modeling, Год журнала: 2024, Номер 64(15), С. 5922 - 5930

Опубликована: Июль 16, 2024

Computational approaches are widely applied in drug discovery to explore properties related bioactivity, physiochemistry, and toxicology. Over at least the last 20 years, exploitation of machine learning on molecular data sets has been used understand structure–activity relationships that exist between biomolecules druggable targets. More recently, these methods have also seen application for phenotypic screening neglected diseases such as tuberculosis malaria. Herein, we apply build quantum Quantitative Structure Activity Relationship models from antimalarial sets. There is a continual need new antimalarials address resistance, readily available vitro could be utilized with newer develop. Furthermore, relatively method uses computer perform calculations. First, present classical-quantum hybrid computational approach by building Latent Bernoulli Autoencoder model compressing bit-vector descriptors size can adapted computers classification tasks limited loss embedded information. Second, our feature map compression algorithms, including completely novel algorithm no analogy classical computers: Quantum Fourier Transform Classifier. We both small-molecule simulation software then benchmark against approaches. While there many challenges currently facing development reliable computers, results demonstrate potential use this technology field discovery.

Язык: Английский

---

Hepatoprotective activity of Alstonia boonei (De Wild) stem bark in isoniazid-induced Wistar rats: antioxidant, anti-inflammatory and in silico evaluations

Pharmacological Research - Modern Chinese Medicine, Год журнала: 2024, Номер unknown, С. 100558 - 100558

Опубликована: Дек. 1, 2024

Язык: Английский

---

A MULTIVARIATE INTERPOLATION APPROACH FOR PREDICTING DRUG LD50 VALUE

Ankara Universitesi Eczacilik Fakultesi Dergisi, Год журнала: 2023, Номер 48(1), С. 3 - 3

Опубликована: Окт. 12, 2023

Objective: The present study aimed to develop a multivariate interpolation based on the quantitative structure-toxicity relationship (QSTR) that can accurately predict oral median lethal dose (LD50) values of drugs in mice by considering five different toxicologic endpoints. Material and Method: A mathematical model was created using comprehensive dataset comprising LD50 from 319 pharmaceuticals belonging various pharmacological classes. We developed polynomial range for pharmaceuticals. employed technique called two-variable interpolation. This method allowed us estimate approximate function at any point within two-dimensional (2D) space utilizing equation. Result Discussion: resulting demonstrated ability new or untested drugs, rendering it valuable tool early stages drug development. Ghose-Crippen-Viswanadhan octanol-water partition coefficient (ALogP) Molecular Weight (MW) were selected as suitable descriptors building best QSAR model. Based our evaluation, achieved an overall success rate 86.73%. Compared traditional experimental methods determination, this innovative approach offers time cost efficiency while reducing animal testing requirements. Our improve safety, optimize dosage regimens, assist decision-making processes during preclinical studies provided reliable efficient preliminary acute toxicity assessments.

Язык: Английский

---

Predictionof Environmental FateandToxicityofInsecticidesUsing Multi‐Target QSAR Approach

Chemistry & Biodiversity, Год журнала: 2023, Номер 21(1)

Опубликована: Дек. 18, 2023

Abstract Ecotoxicological risk assessments form the foundation of regulatory decisions for industrial chemicals used in various sectors. In this study, a multi‐target‐QSAR model established by backpropagation neural network trained with Levenberg‐Marquardt (LM) algorithm was to construct statistically robust and easily interpretable Mt‐QSAR high external predictability simultaneous prediction environmental fate octanol‐water partition coefficient (LogP), (BCF) acute oral toxicity mammals birds (LD 50rat ) 50bird wide range chemical structural classes insecticides. Principal component analysis performed on descriptors selected SW‐MLR method, PCs were constructing SW‐MLR‐PCA‐ANN model. The developed well‐trained (RMSE=0.83, MPE=0.004, CCC=0.82, IIC=0.78, R 2 =0.69) as indicated validation parameters (RMSE=0.93, MPE=0.008, CCC=0.77, IIC=0.68, =0.61). AD also defined identify most reliable predictions. Finally, missing values dataset aforementioned targets predicted using constructed proposed approach can be new insecticides, especially ones that haven′t been tested yet.

Язык: Английский

---