scholarly journals Selection of safe artemisinin derivatives using a machine learning-based cardiotoxicity platform and in vitro and in vivo validation

2021 ◽  
Author(s):  
Onat Kadioglu ◽  
Sabine M. Klauck ◽  
Edmond Fleischer ◽  
Letian Shan ◽  
Thomas Efferth
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
Artemisia Annua ◽  
Reproductive Toxicity ◽  
Toxicity Prediction ◽  
Artemisinin Derivatives ◽  
Drug Candidates

AbstractThe majority of drug candidates fails the approval phase due to unwanted toxicities and side effects. Establishment of an effective toxicity prediction platform is of utmost importance, to increase the efficiency of the drug discovery process. For this purpose, we developed a toxicity prediction platform with machine-learning strategies. Cardiotoxicity prediction was performed by establishing a model with five parameters (arrhythmia, cardiac failure, heart block, hypertension, myocardial infarction) and additional toxicity predictions such as hepatotoxicity, reproductive toxicity, mutagenicity, and tumorigenicity are performed by using Data Warrior and Pro-Tox-II software. As a case study, we selected artemisinin derivatives to evaluate the platform and to provide a list of safe artemisinin derivatives. Artemisinin from Artemisia annua was described first as an anti-malarial compound and later its anticancer properties were discovered. Here, random forest feature selection algorithm was used for the establishment of cardiotoxicity models. High AUC scores above 0.830 were achieved for all five cardiotoxicity indications. Using a chemical library of 374 artemisinin derivatives as a case study, 7 compounds (deoxydihydro-artemisinin, 3-hydroxy-deoxy-dihydroartemisinin, 3-desoxy-dihydroartemisinin, dihydroartemisinin-furano acetate-d3, deoxyartemisinin, artemisinin G, artemisinin B) passed the toxicity filtering process for hepatotoxicity, mutagenicity, tumorigenicity, and reproductive toxicity in addition to cardiotoxicity. Experimental validation with the cardiomyocyte cell line AC16 supported the findings from the in silico cardiotoxicity model predictions. Transcriptomic profiling of AC16 cells upon artemisinin B treatment revealed a similar gene expression profile as that of the control compound, dexrazoxane. In vivo experiments with a Zebrafish model further substantiated the in silico and in vitro data, as only slight cardiotoxicity in picomolar range was observed. In conclusion, our machine-learning approach combined with in vitro and in vivo experimentation represents a suitable method to predict cardiotoxicity of drug candidates.

scholarly journals Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2505
Author(s):  
Raheem Remtulla ◽  
Sanjoy Kumar Das ◽  
Leonard A. Levin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
In Silico ◽  
Disulfide Bonds ◽  
Oral Absorption ◽  
In Vivo Studies ◽  
Drug Candidates ◽  
In Silico Methods

Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.

scholarly journals Combining species specific in vitro & in silico models to predict in vivo food effect in a preclinical stage – case study of Venetoclax

2021 ◽  
pp. 105840
Author(s):  
Laura J. Henze ◽  
Niklas J. Koehl ◽  
Joseph P. O'Shea ◽  
René Holm ◽  
Maria Vertzoni ◽  
...  
Keyword(s):  
In Silico ◽  
Food Effect ◽  
Preclinical Stage ◽  
Species Specific ◽  
In Silico Models

scholarly journals AMPing Up the Search: A Structural and Functional Repository of Antimicrobial Peptides for Biofilm Studies, and a Case Study of Its Application to Corynebacterium striatum, an Emerging Pathogen

2021 ◽  
Vol 11 ◽  
Author(s):  
Shreeya Mhade ◽  
Stutee Panse ◽  
Gandhar Tendulkar ◽  
Rohit Awate ◽  
Yatindrapravanan Narasimhan ◽  
...  
Keyword(s):  
Antimicrobial Peptides ◽  
In Silico ◽  
Emerging Pathogen ◽  
Preference Scale ◽  
Modeling Tools ◽  
Interaction Models ◽  
Corynebacterium Striatum

Antimicrobial peptides (AMPs) have been recognized for their ability to target processes important for biofilm formation. Given the vast array of AMPs, identifying potential anti-biofilm candidates remains a significant challenge, and prompts the need for preliminary in silico investigations prior to extensive in vitro and in vivo studies. We have developed Biofilm-AMP (B-AMP), a curated 3D structural and functional repository of AMPs relevant to biofilm studies. In its current version, B-AMP contains predicted 3D structural models of 5544 AMPs (from the DRAMP database) developed using a suite of molecular modeling tools. The repository supports a user-friendly search, using source, name, DRAMP ID, and PepID (unique to B-AMP). Further, AMPs are annotated to existing biofilm literature, consisting of a vast library of over 10,000 articles, enhancing the functional capabilities of B-AMP. To provide an example of the usability of B-AMP, we use the sortase C biofilm target of the emerging pathogen Corynebacterium striatum as a case study. For this, 100 structural AMP models from B-AMP were subject to in silico protein-peptide molecular docking against the catalytic site residues of the C. striatum sortase C protein. Based on docking scores and interacting residues, we suggest a preference scale using which candidate AMPs could be taken up for further in silico, in vitro and in vivo testing. The 3D protein-peptide interaction models and preference scale are available in B-AMP. B-AMP is a comprehensive structural and functional repository of AMPs, and will serve as a starting point for future studies exploring AMPs for biofilm studies. B-AMP is freely available to the community at https://b-amp.karishmakaushiklab.com and will be regularly updated with AMP structures, interaction models with potential biofilm targets, and annotations to biofilm literature.

scholarly journals Artemisia annua L. extracts prevent in vitro replication of SARS-CoV-2

2021 ◽  
Author(s):  
Manoj S Nair ◽  
Yaoxing Huang ◽  
David A Fidock ◽  
Stephen J Polyak ◽  
Jessica Wagoner ◽  
...  
Keyword(s):  
Antiviral Activity ◽  
Antimalarial Drug ◽  
Artemisia Annua ◽  
Cost Effective ◽  
Hot Water ◽  
Total Flavonoids ◽  
Leaf Extracts ◽  
Artemisinin Derivatives

SARS-CoV-2 (Covid-19) globally has infected and killed millions of people. Besides remdesivir, there are no approved small molecule-based therapeutics. Here we show that extracts of the medicinal plant, Artemisia annua L., which produces the antimalarial drug artemisinin, prevents SARS-CoV-2 replication in vitro. We measured antiviral activity of dried leaf extracts of seven cultivars of A. annua sourced from four continents. Hot-water leaf extracts based on artemisinin, total flavonoids, or dry leaf mass showed antiviral activity with IC50 values of 0.1-8.7 μM, 0.01-0.14 μg, and 23.4-57.4 μg, respectively. One sample was >12 years old, but still active. While all hot water extracts were effective, concentrations of artemisinin and total flavonoids varied by nearly 100-fold in the extracts and antiviral efficacy was inversely correlated to artemisinin and total flavonoid contents. Artemisinin alone showed an estimated IC50 of about 70 μM, and antimalarial artemisinin derivatives artesunate, artemether, and dihydroartemisinin were ineffective or cytotoxic at elevated micromolar concentrations. In contrast, the antimalarial drug amodiaquine had an IC50 = 5.8 μM. The extracts had minimal effects on infection of Vero E6 or Calu-3 cells by a reporter virus pseudotyped by the SARS-CoV-2 spike protein. There was no cytotoxicity within an order of magnitude of the antiviral IC90 values. Results suggest the active component in the extracts is likely something besides artemisinin or is a combination of components acting synergistically to block post-entry viral infection. Further studies will determine in vivo efficacy to assess whether A. annua might provide a cost-effective therapeutic to treat SARS-CoV-2 infections.

Molecular Modeling Approaches for the Prediction of Selected Pharmacokinetic Properties

2019 ◽  
Vol 18 (26) ◽  
pp. 2230-2238 ◽  
Author(s):  
Emilio S. Petito ◽  
David J.R. Foster ◽  
Michael B. Ward ◽  
Matthew J. Sykes
Keyword(s):  
Molecular Modeling ◽  
In Silico ◽  
Volume Of Distribution ◽  
Future Directions ◽  
Unbound Fraction ◽  
Drug Candidates ◽  
New Methods ◽  
Pharmacokinetic Properties

Poor profiles of potential drug candidates, including pharmacokinetic properties, have been acknowledged as a significant hindrance to the development of modern therapeutics. Contemporary drug discovery and development would be incomplete without the aid of molecular modeling (in-silico) techniques, allowing the prediction of pharmacokinetic properties such as clearance, unbound fraction, volume of distribution and bioavailability. As with all models, in-silico approaches are subject to their interpretability, a trait that must be balanced with accuracy when considering the development of new methods. The best models will always require reliable data to inform them, presenting significant challenges, particularly when appropriate in-vitro or in-vivo data may be difficult or time-consuming to obtain. This article seeks to review some of the key in-silico techniques used to predict key pharmacokinetic properties and give commentary on the current and future directions of the field.

ABSORPTION AND METABOLISM OF ASTRAGALI RADIX DECOCTION: IN SILICO, IN VITRO, AND A CASE STUDY IN VIVO

2006 ◽  
Vol 34 (6) ◽  
pp. 913-924 ◽  
Author(s):  
Feng Xu ◽  
Yue Zhang ◽  
Shengyuan Xiao ◽  
Xiaowei Lu ◽  
Donghui Yang ◽  
...  
Keyword(s):  
In Silico ◽  
Astragali Radix

scholarly journals In silico prospection of antineoplastic molecules from the Artemisia annua species

2021 ◽  
Vol 7 (19) ◽  
Author(s):  
Isabela Sacienti Lavezo ◽  
Juracy Cirino de Souza Neto ◽  
Túlio Nunes Pinto ◽  
Leonardo Luiz Borges
Keyword(s):  
In Silico ◽  
Artemisia Annua ◽  
Biological Effects ◽  
Low Cost ◽  
Target Prediction ◽  
Molecular Target ◽  
New Drugs ◽  
Antineoplastic Activity

Lung cancer kills the most men and the second that kills the most women (behind only breast cancer). The in silico study makes it possible to search for new drugs at low cost, with a greater possibility of rapid manufacturing and a lower future cost for their manufacture. The objective of this study was to analyze an antineoplastic activity of the compounds of Artemisia annua to obtain an active substance that can reach the molecular target of the cancer cells. Compounds with antineoplastic effects were selected using Scielo, PubMed, and ScienceDirect platforms. Afterward, the first screening of compound compounds was performed with a high ability to predict biological and pharmacological activity through the PASS Prediction, Pubchem, and Swiss ADME platforms. After the current screening, we determined the toxicological and molecular target prediction by the Portox II and Swiss Target Prediction platforms. As a final part, molecular docking and redocking were performed for a compound using the PDB server and the GOLD Suite 5.7.0 program. For another, we completed the pharmacophoric mapping using the Binding DB and PharmaGist database. The compounds scopoletin and caffeic acid were the most promising structures in silico models capable of interacting with EGFR (epidermal growth factor) and MM-9 (metalloproteinase type 9), respectively. The results obtained that these structures are promising to be tested in in vitro and in vivo tests about the antineoplastic activity. In addition, in silico analyses help to understand the biological effects of A. annua extracts regarding antineoplastic evidence.

scholarly journals Carotid Bifurcation With Tandem Stenosis—A Patient-Specific Case Study Combined in vivo Imaging, in vitro Histology and in silico Simulation

2019 ◽  
Vol 7 ◽  
Author(s):  
Jiaqiu Wang ◽  
Phani Kumari Paritala ◽  
Jessica Benitez Mendieta ◽  
Yuantong Gu ◽  
Owen Christopher Raffel ◽  
...  
Keyword(s):  
In Vivo Imaging ◽  
In Silico ◽  
Carotid Bifurcation ◽  
Patient Specific ◽  
Tandem Stenosis ◽  
In Silico Simulation
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