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1.
Anal Chem ; 2021 Nov 23.
Artigo em Inglês | MEDLINE | ID: mdl-34812602

RESUMO

Ultraviolet-visible (UV-Vis) absorption spectra are routinely collected as part of high-performance liquid chromatography (HPLC) analysis systems and can be used to identify chemical reaction products by comparison to the reference spectra. Here, we present UV-adVISor as a new computational tool for predicting the UV-Vis spectra from a molecule's structure alone. UV-Vis prediction was approached as a sequence-to-sequence problem. We utilized Long-Short Term Memory and attention-based neural networks with Extended Connectivity Fingerprint Diameter 6 or molecule SMILES to generate predictive models for the UV spectra. We have produced two spectrum datasets (dataset I, N = 949, and dataset II, N = 2222) using different compound collections and spectrum acquisition methods to train, validate, and test our models. We evaluated the prediction accuracy of the complete spectra by the correspondence of wavelengths of absorbance maxima and with a series of statistical measures (the best test set median model parameters are in parentheses for model II), including RMSE (0.064), R2 (0.71), and dynamic time warping (DTW, 0.194) of the entire spectrum curve. Scrambling molecule structures with the experimental spectra during training resulted in a degraded R2, confirming the utility of the approaches for prediction. UV-adVISor is able to provide fast and accurate predictions for libraries of compounds.

3.
Drug Discov Today ; 2021 Oct 27.
Artigo em Inglês | MEDLINE | ID: mdl-34718207

RESUMO

The conventional drug discovery pipeline has proven to be unsustainable for rare diseases. Herein, we discuss recent advances in biomedical knowledge mining applied to discovering therapeutics for rare diseases. We summarize current chemogenomics data of relevance to rare diseases and provide a perspective on the effectiveness of machine learning (ML) and biomedical knowledge graph mining in rare disease drug discovery. We illustrate the power of these methodologies using a chordoma case study. We expect that a broader application of knowledge graph mining and artificial intelligence (AI) approaches will expedite the discovery of viable drug candidates against both rare and common diseases.

4.
Mol Pharmacol ; 100(6): 548-557, 2021 12.
Artigo em Inglês | MEDLINE | ID: mdl-34503974

RESUMO

Equilibrative nucleoside transporters (ENTs) are present at the blood-testis barrier (BTB), where they can facilitate antiviral drug disposition to eliminate a sanctuary site for viruses detectable in semen. The purpose of this study was to investigate ENT-drug interactions with three nucleoside analogs, remdesivir, molnupiravir, and molnupiravir's active metabolite, ß-d-N4-hydroxycytidine (EIDD-1931), and four non-nucleoside molecules repurposed as antivirals for coronavirus disease 2019 (COVID-19). The study used three-dimensional pharmacophores for ENT1 and ENT2 substrates and inhibitors and Bayesian machine learning models to identify potential interactions with these transporters. In vitro transport experiments demonstrated that remdesivir was the most potent inhibitor of ENT-mediated [3H]uridine uptake (ENT1 IC50: 39 µM; ENT2 IC50: 77 µM), followed by EIDD-1931 (ENT1 IC50: 259 µM; ENT2 IC50: 467 µM), whereas molnupiravir was a modest inhibitor (ENT1 IC50: 701 µM; ENT2 IC50: 851 µM). Other proposed antivirals failed to inhibit ENT-mediated [3H]uridine uptake below 1 mM. Remdesivir accumulation decreased in the presence of 6-S-[(4-nitrophenyl)methyl]-6-thioinosine (NBMPR) by 30% in ENT1 cells (P = 0.0248) and 27% in ENT2 cells (P = 0.0054). EIDD-1931 accumulation decreased in the presence of NBMPR by 77% in ENT1 cells (P = 0.0463) and by 64% in ENT2 cells (P = 0.0132), which supported computational predictions that both are ENT substrates that may be important for efficacy against COVID-19. NBMPR failed to decrease molnupiravir uptake, suggesting that ENT interaction is likely inhibitory. Our combined computational and in vitro data can be used to identify additional ENT-drug interactions to improve our understanding of drugs that can circumvent the BTB. SIGNIFICANCE STATEMENT: This study identified remdesivir and EIDD-1931 as substrates of equilibrative nucleoside transporters 1 and 2. This provides a potential mechanism for uptake of these drugs into cells and may be important for antiviral potential in the testes and other tissues expressing these transporters.

5.
J Chem Inf Model ; 61(9): 4125-4130, 2021 09 27.
Artigo em Inglês | MEDLINE | ID: mdl-34516123

RESUMO

A recent publication in Science has proposed that cationic amphiphilic drugs repurposed for COVID-19 typically use phosholipidosis as their antiviral mechanism of action in cells but will have no in vivo efficacy. On the contrary, our viewpoint, supported by additional experimental data for similar cationic amphiphilic drugs, indicates that many of these molecules have both in vitro and in vivo efficacy with no reported phospholipidosis, and therefore, this class of compounds should not be avoided but further explored, as we continue the search for broad spectrum antivirals.


Assuntos
COVID-19 , Lipidoses , Preparações Farmacêuticas , Antivirais/toxicidade , Humanos , Lipidoses/tratamento farmacológico , Fosfolipídeos , SARS-CoV-2
6.
J Chem Inf Model ; 61(9): 4224-4235, 2021 09 27.
Artigo em Inglês | MEDLINE | ID: mdl-34387990

RESUMO

With the rapidly evolving SARS-CoV-2 variants of concern, there is an urgent need for the discovery of further treatments for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need, and numerous compounds have already been selected for in vitro testing by several groups. These have led to a growing database of molecules with in vitro activity against the virus. Machine learning models can assist drug discovery through prediction of the best compounds based on previously published data. Herein, we have implemented several machine learning methods to develop predictive models from recent SARS-CoV-2 in vitro inhibition data and used them to prioritize additional FDA-approved compounds for in vitro testing selected from our in-house compound library. From the compounds predicted with a Bayesian machine learning model, lumefantrine, an antimalarial was selected for testing and showed limited antiviral activity in cell-based assays while demonstrating binding (Kd 259 nM) to the spike protein using microscale thermophoresis. Several other compounds which we prioritized have since been tested by others and were also found to be active in vitro. This combined machine learning and in vitro testing approach can be expanded to virtually screen available molecules with predicted activity against SARS-CoV-2 reference WIV04 strain and circulating variants of concern. In the process of this work, we have created multiple iterations of machine learning models that can be used as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 with over 500 compounds is now freely available at www.assaycentral.org.


Assuntos
COVID-19 , SARS-CoV-2 , Teorema de Bayes , Humanos , Aprendizado de Máquina , Simulação de Acoplamento Molecular
7.
ACS Infect Dis ; 7(8): 2508-2521, 2021 08 13.
Artigo em Inglês | MEDLINE | ID: mdl-34342426

RESUMO

We present the application of Bayesian modeling to identify chemical tools and/or drug discovery entities pertinent to drug-resistant Staphylococcus aureus infections. The quinoline JSF-3151 was predicted by modeling and then empirically demonstrated to be active against in vitro cultured clinical methicillin- and vancomycin-resistant strains while also exhibiting efficacy in a mouse peritonitis model of methicillin-resistant S. aureus infection. We highlight the utility of an intrabacterial drug metabolism (IBDM) approach to probe the mechanism by which JSF-3151 is transformed within the bacteria. We also identify and then validate two mechanisms of resistance in S. aureus: one mechanism involves increased expression of a lipocalin protein, and the other arises from the loss of function of an azoreductase. The computational and experimental approaches, discovery of an antibacterial agent, and elucidated resistance mechanisms collectively hold promise to advance our understanding of therapeutic regimens for drug-resistant S. aureus.


Assuntos
Staphylococcus aureus Resistente à Meticilina , Preparações Farmacêuticas , Infecções Estafilocócicas , Animais , Teorema de Bayes , Camundongos , Infecções Estafilocócicas/tratamento farmacológico , Staphylococcus aureus
8.
Chem Soc Rev ; 50(16): 9121-9151, 2021 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-34212944

RESUMO

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.


Assuntos
COVID-19/tratamento farmacológico , Simulação por Computador , Desenho de Fármacos , Descoberta de Drogas/métodos , Reposicionamento de Medicamentos , Antivirais/uso terapêutico , COVID-19/virologia , Ensaios Clínicos como Assunto , Humanos , Pandemias , SARS-CoV-2/efeitos dos fármacos
10.
J Pharmacol Exp Ther ; 379(1): 96-107, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34253645

RESUMO

In the wake of the COVID-19 pandemic, drug repurposing has been highlighted for rapid introduction of therapeutics. Proposed drugs with activity against SARS-CoV-2 include compounds with positive charges at physiologic pH, making them potential targets for the organic cation secretory transporters of kidney and liver, i.e., the basolateral organic cation transporters, OCT1 and OCT2; and the apical multidrug and toxin extruders, MATE1 and MATE2-K. We selected several compounds proposed to have in vitro activity against SARS-CoV-2 (chloroquine, hydroxychloroquine, quinacrine, tilorone, pyronaridine, cetylpyridinium, and miramistin) to test their interaction with OCT and MATE transporters. We used Bayesian machine learning models to generate predictions for each molecule with each transporter and also experimentally determined IC50 values for each compound against labeled substrate transport into CHO cells that stably expressed OCT2, MATE1, or MATE2-K using three structurally distinct substrates (atenolol, metformin and 1-methyl-4-phenylpyridinium) to assess the impact of substrate structure on inhibitory efficacy. For the OCTs substrate identity influenced IC50 values, although the effect was larger and more systematic for OCT2. In contrast, inhibition of MATE1-mediated transport was largely insensitive to substrate identity. Unlike MATE1, inhibition of MATE2-K was influenced, albeit modestly, by substrate identity. Maximum unbound plasma concentration/IC50 ratios were used to identify potential clinical DDI recommendations; all the compounds interacted with the OCT/MATE secretory pathway, most with sufficient avidity to represent potential DDI issues for secretion of cationic drugs. This should be considered when proposing cationic agents as repurposed antivirals. SIGNIFICANCE STATEMENT: Drugs proposed as potential COVID-19 therapeutics based on in vitro activity data against SARS-CoV-2 include compounds with positive charges at physiological pH, making them potential interactors with the OCT/MATE renal secretory pathway. We tested seven such molecules as inhibitors of OCT1/2 and MATE1/2-K. All the compounds blocked transport activity regardless of substrate used to monitor activity. Suggesting that plasma concentrations achieved by normal clinical application of the test agents could be expected to influence the pharmacokinetics of selected cationic drugs.


Assuntos
Antivirais/farmacologia , Proteínas de Transporte de Cátions Orgânicos/metabolismo , SARS-CoV-2/efeitos dos fármacos , Animais , Compostos de Benzalcônio/farmacologia , Células CHO , Cetilpiridínio/farmacologia , Cloroquina/análogos & derivados , Cloroquina/farmacologia , Cricetinae , Cricetulus , Naftiridinas/farmacologia , Proteínas de Transporte de Cátions Orgânicos/efeitos dos fármacos , Quinacrina/farmacologia , Tilorona/farmacologia
11.
J Chem Inf Model ; 61(8): 3804-3813, 2021 08 23.
Artigo em Inglês | MEDLINE | ID: mdl-34286575

RESUMO

Yellow fever (YF) is an acute viral hemorrhagic disease transmitted by infected mosquitoes. Large epidemics of YF occur when the virus is introduced into heavily populated areas with high mosquito density and low vaccination coverage. The lack of a specific small molecule drug treatment against YF as well as for homologous infections, such as zika and dengue, highlights the importance of these flaviviruses as a public health concern. With the advancement in computer hardware and bioactivity data availability, new tools based on machine learning methods have been introduced into drug discovery, as a means to utilize the growing high throughput screening (HTS) data generated to reduce costs and increase the speed of drug development. The use of predictive machine learning models using previously published data from HTS campaigns or data available in public databases, can enable the selection of compounds with desirable bioactivity and absorption, distribution, metabolism, and excretion profiles. In this study, we have collated cell-based assay data for yellow fever virus from the literature and public databases. The data were used to build predictive models with several machine learning methods that could prioritize compounds for in vitro testing. Five molecules were prioritized and tested in vitro from which we have identified a new pyrazolesulfonamide derivative with EC50 3.2 µM and CC50 24 µM, which represents a new scaffold suitable for hit-to-lead optimization that can expand the available drug discovery candidates for YF.


Assuntos
Febre Amarela , Infecção por Zika virus , Zika virus , Animais , Antivirais/farmacologia , Descoberta de Drogas , Aprendizado de Máquina , Vírus da Febre Amarela
12.
Curr Opin Chem Biol ; 65: 74-84, 2021 Jul 15.
Artigo em Inglês | MEDLINE | ID: mdl-34274565

RESUMO

Drug repurposing aims to find new uses for already existing and approved drugs. We now provide a brief overview of recent developments in drug repurposing using machine learning alongside other computational approaches for comparison. We also highlight several applications for cancer using kinase inhibitors, Alzheimer's disease as well as COVID-19.

13.
PLoS One ; 16(7): e0253852, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34255797

RESUMO

Abcg2/Bcrp and Abcb1a/Pgp are xenobiotic efflux transporters limiting substrate permeability in the gastrointestinal system and brain, and increasing renal and hepatic drug clearance. The systemic impact of Bcrp and Pgp ablation on metabolic homeostasis of endogenous substrates is incompletely understood. We performed untargeted metabolomics of cerebrospinal fluid (CSF) and plasma, transcriptomics of brain, liver and kidney from male Sprague Dawley rats (WT) and Bcrp/Pgp double knock-out (dKO) rats, and integrated metabolomic/transcriptomic analysis to identify putative substrates and perturbations in canonical metabolic pathways. A predictive Bayesian machine learning model was used to predict in silico those metabolites with greater substrate-like features for either transporters. The CSF and plasma levels of 169 metabolites, nutrients, signaling molecules, antioxidants and lipids were significantly altered in dKO rats, compared to WT rats. These metabolite changes suggested alterations in histidine, branched chain amino acid, purine and pyrimidine metabolism in the dKO rats. Levels of methylated and sulfated metabolites and some primary bile acids were increased in dKO CSF or plasma. Elevated uric acid levels appeared to be a primary driver of changes in purine and pyrimidine biosynthesis. Alterations in Bcrp/Pgp dKO CSF levels of antioxidants, precursors of neurotransmitters, and uric acid suggests the transporters may contribute to the regulation of a healthy central nervous system in rats. Microbiome-generated metabolites were found to be elevated in dKO rat plasma and CSF. The altered dKO metabolome appeared to cause compensatory transcriptional change in urate biosynthesis and response to lipopolysaccharide in brain, oxidation-reduction processes and response to oxidative stress and porphyrin biosynthesis in kidney, and circadian rhythm genes in liver. These findings present insight into endogenous functions of Bcrp and Pgp, the impact that transporter substrates, inhibitors or polymorphisms may have on metabolism, how transporter inhibition could rewire drug sensitivity indirectly through metabolic changes, and identify functional Bcrp biomarkers.


Assuntos
Subfamília B de Transportador de Cassetes de Ligação de ATP/deficiência , Membro 2 da Subfamília G de Transportadores de Cassetes de Ligação de ATP/deficiência , Subfamília B de Transportador de Cassetes de Ligação de ATP/genética , Membro 2 da Subfamília G de Transportadores de Cassetes de Ligação de ATP/genética , Animais , Encéfalo/metabolismo , Perfilação da Expressão Gênica , Técnicas de Inativação de Genes , Histidina/metabolismo , Rim/metabolismo , Fígado/metabolismo , Masculino , Taxa de Depuração Metabólica , Metabolômica , Purinas/metabolismo , Pirimidinas/metabolismo , Ratos , Ratos Transgênicos
14.
ACS Omega ; 6(24): 16253, 2021 Jun 22.
Artigo em Inglês | MEDLINE | ID: mdl-34179670

RESUMO

[This corrects the article DOI: 10.1021/acsomega.0c05591.].

16.
Drug Metab Dispos ; 49(7): 479-489, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-33980604

RESUMO

Equilibrativenucleoside transporters (ENTs) participate in the pharmacokinetics and disposition of nucleoside analog drugs. Understanding drug interactions with the ENTs may inform and facilitate the development of new drugs, including chemotherapeutics and antivirals that require access to sanctuary sites such as the male genital tract. This study created three-dimensional pharmacophores for ENT1 and ENT2 substrates and inhibitors using Kt and IC50 data curated from the literature. Substrate pharmacophores for ENT1 and ENT2 are distinct, with partial overlap of hydrogen bond donors, whereas the inhibitor pharmacophores predominantly feature hydrogen bond acceptors. Mizoribine and ribavirin mapped to the ENT1 substrate pharmacophore and proved to be substrates of the ENTs. The presence of the ENT-specific inhibitor 6-S-[(4-nitrophenyl)methyl]-6-thioinosine (NBMPR) decreased mizoribine accumulation in ENT1 and ENT2 cells (ENT1, ∼70% decrease, P = 0.0046; ENT2, ∼50% decrease, P = 0.0012). NBMPR also decreased ribavirin accumulation in ENT1 and ENT2 cells (ENT1: ∼50% decrease, P = 0.0498; ENT2: ∼30% decrease, P = 0.0125). Darunavir mapped to the ENT1 inhibitor pharmacophore and NBMPR did not significantly influence darunavir accumulation in either ENT1 or ENT2 cells (ENT1: P = 0.28; ENT2: P = 0.53), indicating that darunavir's interaction with the ENTs is limited to inhibition. These computational and in vitro models can inform compound selection in the drug discovery and development process, thereby reducing time and expense of identification and optimization of ENT-interacting compounds. SIGNIFICANCE STATEMENT: This study developed computational models of human equilibrative nucleoside transporters (ENTs) to predict drug interactions and validated these models with two compounds in vitro. Identification and prediction of ENT1 and ENT2 substrates allows for the determination of drugs that can penetrate tissues expressing these transporters.

17.
J Chem Inf Model ; 61(6): 2641-2647, 2021 06 28.
Artigo em Inglês | MEDLINE | ID: mdl-34032436

RESUMO

The growing quantity of public and private data sets focused on small molecules screened against biological targets or whole organisms provides a wealth of drug discovery relevant data. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large data sets with thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, which is widely used in drug discovery, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on a QC. Here, we show how to achieve compression with data sets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands of molecules (whole cell screening data sets for plague and M. tuberculosis) with SVM and the data reuploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This study illustrates the steps needed in order to be "quantum computer ready" in order to apply quantum computing to drug discovery and to provide the foundation on which to build this field.


Assuntos
COVID-19 , Descoberta de Drogas , Algoritmos , Metodologias Computacionais , Humanos , Aprendizado de Máquina , Teoria Quântica , SARS-CoV-2 , Máquina de Vetores de Suporte
18.
ACS Chem Neurosci ; 12(12): 2247-2253, 2021 06 16.
Artigo em Inglês | MEDLINE | ID: mdl-34028255

RESUMO

The ability to calculate whether small molecules will cross the blood-brain barrier (BBB) is an important task for companies working in neuroscience drug discovery. For a decade, scientists have relied on relatively simplistic rules such as Pfizer's central nervous system multiparameter optimization models (CNS-MPO) for guidance during the drug selection process. In parallel, there has been a continued development of more sophisticated machine learning models that utilize different molecular descriptors and algorithms; however, these models represent a "black box" and are generally less interpretable. In both cases, these methods predict the ability of small molecules to cross the BBB using the molecular structure information on its own without in vitro or in vivo data. We describe here the implementation of two versions of Pfizer's algorithm (Pf-MPO.v1 and Pf-MPO.v2) and compare it with a Bayesian machine learning model of BBB penetration trained on a data set of 2296 active and inactive compounds using extended connectivity fingerprint descriptors. The predictive ability of these approaches was compared with 40 known CNS active drugs initially used by Pfizer as their positive set for validation of the Pf-MPO.v1 score. 37/40 (92.5%) compounds were predicted as active by the Bayesian model, while only 30/40 (75%) received a desirable Pf-MPO.v1 score ≥4 and 33/40 (82.5%) received a desirable Pf-MPO.v2 score ≥4, suggesting the Bayesian model is more accurate than MPO algorithms. This also indicates machine learning models are more flexible and have better predictive power for BBB penetration than simple rule sets that require multiple, accurate descriptor calculations. Our machine learning model statistics are comparable to recent published studies. We describe the implications of these findings and how machine learning may have a role alongside more interpretable methods.


Assuntos
Barreira Hematoencefálica , Sistema Nervoso Central , Teorema de Bayes , Fármacos do Sistema Nervoso Central , Aprendizado de Máquina
19.
ACS Med Chem Lett ; 12(5): 774-781, 2021 May 13.
Artigo em Inglês | MEDLINE | ID: mdl-34055225

RESUMO

Opportunistic infections from pathogenic fungi present a major challenge to healthcare because of a very limited arsenal of antifungal drugs, an increasing population of immunosuppressed patients, and increased prevalence of resistant clinical strains due to overuse of the few available antifungals. Cryptococcal meningitis is a life-threatening opportunistic fungal infection caused by one of two species in the Cryptococcus genus, Cryptococcus neoformans and Cryptococcus gattii. Eighty percent of cryptococcosis diseases are caused by C. neoformans that is endemic in the environment. The standard of care is limited to old antifungals, and under a high standard of care, mortality remains between 10 and 30%. We have identified a series of 5-nitro-6-thiocyanatopyrimidine antifungal drug candidates using in vitro and computational machine learning approaches. These compounds can inhibit C. neoformans growth at submicromolar levels, are effective against fluconazole-resistant C. neoformans and a clinical strain of C. gattii, and are not antagonistic with currently approved antifungals.

20.
Environ Health Perspect ; 129(4): 47013, 2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33929906

RESUMO

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

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