scholarly journals IDentif.AI: Artificial Intelligence Pinpoints Remdesivir in Combination with Ritonavir and Lopinavir as an Optimal Regimen Against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)

2020 ◽  
Author(s):  
Agata Blasiak ◽  
Jhin Jieh Lim ◽  
Shirley Gek Kheng Seah ◽  
Theodore Kee ◽  
Alexandria Remus ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Severe Acute Respiratory Syndrome ◽  
Combination Therapy ◽  
Rapid Development ◽  
Drug Combinations ◽  
Drug Candidate ◽  
Optimal Drug ◽  
Order Of Magnitude ◽  
Drug Regimens

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease 2019 (COVID-19) has led to the rapid initiation of urgently needed clinical trials of repurposed drug combinations and monotherapies. These regimens were primarily relying on mechanism-of-action based selection of drugs, many of which have yielded positive in vitro but largely negative clinical outcomes. To overcome this challenge, we report the use of IDentif.AI, a platform that rapidly optimizes infectious disease (ID) combination therapy design using artificial intelligence (AI). In this study, IDentif.AI was implemented on a 12-drug candidate therapy search set representing over 530,000 possible drug combinations. IDentif.AI demonstrated that the optimal combination therapy against SARS-CoV-2 was comprised of remdesivir, ritonavir, and lopinavir, which mediated a 6.5-fold improvement in efficacy over remdesivir alone. Additionally, IDentif.AI showed hydroxychloroquine and azithromycin to be relatively ineffective. The identification of a clinically actionable optimal drug combination was completed within two weeks, with a 3-order of magnitude reduction in the number of tests typically needed. IDentif.AI analysis was also able to independently confirm clinical trial outcomes to date without requiring any data from these trials. The robustness of the IDentif.AI platform suggests that it may be applicable towards rapid development of optimal drug regimens to address current and future outbreaks.

Artificial intelligence in drug combination therapy

2018 ◽  
Vol 20 (4) ◽  
pp. 1434-1448 ◽  
Author(s):  
Igor F Tsigelny
Keyword(s):  
Artificial Intelligence ◽  
Combination Therapy ◽  
Drug Combination ◽  
Drug Combinations ◽  
Machine Learning Algorithms ◽  
Genetic Profile ◽  
Combination Drug ◽  
Area Of Interest ◽  
Drug Combination Therapy ◽  
Optimal Drug

AbstractCurrently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many cases, one drug cannot target all necessary points of intervention. For example, in cancer therapy, a physician often meets a patient having a genomic profile including more than five molecular aberrations. Drug combination therapy has been an area of interest for a while, for example the classical work of Loewe devoted to the synergism of drugs was published in 1928—and it is still used in calculations for optimal drug combinations. More recently, over the past several years, there has been an explosion in the available information related to the properties of drugs and the biomedical parameters of patients. For the drugs, hundreds of 2D and 3D molecular descriptors for medicines are now available, while for patients, large data sets related to genetic/proteomic and metabolomics profiles of the patients are now available, as well as the more traditional data relating to the histology, history of treatments, pretreatment state of the organism, etc. Moreover, during disease progression, the genetic profile can change. Thus, the ability to optimize drug combinations for each patient is rapidly moving beyond the comprehension and capabilities of an individual physician. This is the reason, that biomedical informatics methods have been developed and one of the more promising directions in this field is the application of artificial intelligence (AI). In this review, we discuss several AI methods that have been successfully implemented in several instances of combination drug therapy from HIV, hypertension, infectious diseases to cancer. The data clearly show that the combination of rule-based expert systems with machine learning algorithms may be promising direction in this field.

scholarly journals Anti-Adenovirus Activity of the Medical Intranasal Drug Nazoferon

2021 ◽  
Vol 83 (2) ◽  
pp. 73-81
Author(s):  
O.Yu. Povnitsa ◽  
◽  
L.O. Biliavska ◽  
Yu.B. Pankivska ◽  
S.D. Zagorodnya ◽  
...  
Keyword(s):  
Respiratory Infections ◽  
Low Cost ◽  
Rapid Development ◽  
Colorimetric Method ◽  
Antiviral Drug ◽  
Human Adenovirus ◽  
Human Adenoviruses ◽  
Direct Acting Antiviral ◽  
Optimal Drug

Currently, 90 different types of human adenoviruses (HAdV) are known, which have been classified into seven species from A to G and new adenovirus types continue to emerge. Antigenic diversity of viruses inhibits the process of creating universal vaccines and causes the development of resistance to direct-acting antiviral drugs. In addition to the rapid development of drug resistance, too narrow a range of existing drugs and a significant number of side effects limits the treatment of adenoviral infections. There is currently no specific etiotropic antiviral drug. Therefore, the development of new effective drugs and the selection of the optimal drug for the treatment of infections caused by adenoviruses remain relevant. The aim of the study was to investigate the antiviral properties of the drugs Nazoferon spray and Nazoferon drops in a model of human adenovirus serotype 3. Methods. Determination of cytotoxicity and antiviral action of drugs was performed by standard colorimetric method using MTT. The titer of the virus, synthesized in the presence of drugs was determined by the end point of dilution of the virus, which causes 50% development of the cytopathic effect of the virus on cells (СPE). Results. Low cytotoxicity of Nazoferon spray and Nazoferon drops (manufactured by JSC Farmak, Ukraine) was shown, CC50 is 53854 IU/ml and 54357 IU/ml, respectively. Quantitative and qualitative composition of excipients had no cytotoxic effect. In prophylactic regimens, interferon preparations did not inhibit the reproduction of adenovirus in vitro. Taking into account that most of the virions remain associated with the cells during the reproduction of adenovirus in the cell, we used test to determine infectivity lysates of infected and treated cells. The infectious titer of the synthesized HAdV3 was reduced by 3.2 log10 and 3.7 log10 for Nazoferon spray and drops, respectively. Conclusions. Nazoferon spray and drops can be recommended as anti-adenoviral drugs that block the reproduction of adenovirus, and due to their bioavailability and low cost have significant advantages in the treatment of acute respiratory infections (ARIs) caused by human adenoviruses.

scholarly journals Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis

mBio ◽  
2019 ◽  
Vol 10 (6) ◽  
Author(s):  
Shuyi Ma ◽  
Suraj Jaipalli ◽  
Jonah Larkins-Ford ◽  
Jenny Lohmiller ◽  
Bree B. Aldridge ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Computational Model ◽  
Drug Combinations ◽  
Broth Culture ◽  
Multiple Drug ◽  
Content Type ◽  
Drug Synergy ◽  
Drug Regimens

ABSTRACT The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen Mycobacterium tuberculosis (MTB), coupled with the large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico more than 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c upregulation reduces the antagonism of the bedaquiline-streptomycin combination. A retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (P value = 1 × 10−4) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens. IMPORTANCE Multidrug combination therapy is an important strategy for treating tuberculosis, the world’s deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB that identifies synergistic drug regimens from an immense set of possible drug combinations using the pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.

scholarly journals Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

2020 ◽  
Vol 10 (7) ◽  
pp. 2376 ◽  
Author(s):  
Rob C. van Wijk ◽  
Rami Ayoun Alsoud ◽  
Hans Lennernäs ◽  
Ulrika S. H. Simonsson
Keyword(s):  
Drug Discovery ◽  
Drug Interactions ◽  
Development Strategy ◽  
Drug Exposure ◽  
Rapid Development ◽  
Drug Combinations ◽  
Pharmacodynamic Modeling ◽  
Drug Discovery And Development

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.

scholarly journals Importance of Confirming Data on theIn VivoEfficacy of Novel Antibacterial Drug Regimens against Various Strains of Mycobacterium tuberculosis

2011 ◽  
Vol 56 (2) ◽  
pp. 731-738 ◽  
Author(s):  
Mary A. De Groote ◽  
Veronica Gruppo ◽  
Lisa K. Woolhiser ◽  
Ian M. Orme ◽  
Janet C. Gilliland ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Animal Studies ◽  
Strain Difference ◽  
Drug Combinations ◽  
Antibacterial Drug ◽  
Preclinical Testing ◽  
Content Type ◽  
Drug Regimens

ABSTRACTIn preclinical testing of antituberculosis drugs, laboratory-adapted strains ofMycobacterium tuberculosisare usually used both forin vitroandin vivostudies. However, it is unknown whether the heterogeneity ofM. tuberculosisstocks used by various laboratories can result in different outcomes in tests of antituberculosis drug regimens in animal infection models. In head-to-head studies, we investigated whether bactericidal efficacy results in BALB/c mice infected by inhalation with the laboratory-adapted strains H37Rv and Erdman differ from each other and from those obtained with clinical tuberculosis strains. Treatment of mice consisted of dual and triple drug combinations of isoniazid (H), rifampin (R), and pyrazinamide (Z). The results showed that not all strains gave the samein vivoefficacy results for the drug combinations tested. Moreover, the ranking of HRZ and RZ efficacy results was not the same for the two H37Rv strains evaluated. The magnitude of this strain difference also varied between experiments, emphasizing the risk of drawing firm conclusions for human trials based on single animal studies. The results also confirmed that the antagonism seen within the standard HRZ regimen by some investigators appears to be anM. tuberculosisstrain-specific phenomenon. In conclusion, the specific identity ofM. tuberculosisstrain used was found to be an important variable that can change the apparent outcome ofin vivoefficacy studies in mice. We highly recommend confirmation of efficacy results in late preclinical testing against a differentM. tuberculosisstrain than the one used in the initial mouse efficacy study, thereby increasing confidence to advance potent drug regimens to clinical trials.

scholarly journals Lopinavir-ritonavir is not an effective inhibitor of the main protease activity of SARS-CoV-2 in vitro

2020 ◽  
Author(s):  
Minsu Jang ◽  
Yea-In Park ◽  
Rackhyun Park ◽  
Yeo-Eun Cha ◽  
Sim Namkoong ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Combination Therapy ◽  
Candidate Selection ◽  
Hiv Protease ◽  
Drug Candidate ◽  
Effective Inhibitor ◽  
Clinical Tests ◽  
Main Protease ◽  
Selection For

ABSTRACTCOVID-19 has caused over 900,000 deaths worldwide as of September 2020, and effective medicines are urgently needed. Lopinavir was identified as an inhibitor of the HIV protease, and a lopinavir-ritonavir combination therapy was reported to be beneficial for the treatment of SARS and MERS. However, recent clinical tests could not prove that lopinavir-ritonavir therapy was an effective treatment for COVID-19. In this report, we examined the effect of lopinavir and ritonavir to the activity of the purified main protease (Mpro) protein of SARS- CoV-2, the causative virus of COVID-19. Unexpectedly, lopinavir and ritonavir did not inhibit Mpro activity. These results will aid the drug candidate selection for ongoing and future COVID-19 clinical trials.

scholarly journals In Vitro Assessment of the Antiviral Activity of Ketotifen, Indomethacin and Naproxen, Alone and in Combination, against SARS-CoV-2

Viruses ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 558
Author(s):  
Pantea Kiani ◽  
Andrew Scholey ◽  
Thomas A. Dahl ◽  
Lauren McMann ◽  
Jacqueline M. Iversen ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Severe Acute Respiratory Syndrome ◽  
Antiviral Activity ◽  
Synergistic Effect ◽  
Treatment Options ◽  
Drug Combinations ◽  
Cytotoxic Effects ◽  
Viral Yield ◽  
In Vitro Assessment

The 2019 coronavirus infectious disease (COVID-19) is caused by infection with the new severe acute respiratory syndrome coronavirus (SARS-CoV-2). Currently, the treatment options for COVID-19 are limited. The purpose of the experiments presented here was to investigate the effectiveness of ketotifen, naproxen and indomethacin, alone or in combination, in reducing SARS-CoV-2 replication. In addition, the cytotoxicity of the drugs was evaluated. The findings showed that the combination of ketotifen with indomethacin (SJP-002C) or naproxen both reduce viral yield. Compared to ketotifen alone (60% inhibition at EC50), an increase in percentage inhibition of SARS-CoV-2 to 79%, 83% and 93% was found when co-administered with 25, 50 and 100 μM indomethacin, respectively. Compared to ketotifen alone, an increase in percentage inhibition of SARS-CoV-2 to 68%, 68% and 92% was found when co-administered with 25, 50 and 100 μM naproxen, respectively. For both drug combinations the observations suggest an additive or synergistic effect, compared to administering the drugs alone. No cytotoxic effects were observed for the administered dosages of ketotifen, naproxen, and indomethacin. Further research is warranted to investigate the efficacy of the combination of ketotifen with indomethacin (SJP-002C) or naproxen in the treatment of SARS-CoV-2 infection in humans.

scholarly journals Transcriptomic signatures predict regulators of drug synergy and clinical regimen efficacy against Tuberculosis

2019 ◽  
Author(s):  
Shuyi Ma ◽  
Suraj Jaipalli ◽  
Jonah Larkins-Ford ◽  
Jenny Lohmiller ◽  
Bree B. Aldridge ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Computational Model ◽  
Drug Combinations ◽  
Broth Culture ◽  
Multiple Drug ◽  
P Value ◽  
Drug Synergy ◽  
Drug Regimens

ABSTRACTThe rapid spread of multi-drug resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen M. tuberculosis (MTB), coupled with large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico over 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c up-regulation reduces the antagonism of the bedaquiline-streptomycin combination. Retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (p-value = 1 × 10−4) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens.IMPORTANCEMulti-drug combination therapy is an important strategy for treating tuberculosis, the world’s deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB, which identifies synergistic drug regimens from an immense set of possible drug combinations using pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.

scholarly journals Cutting down the time to identify challenging tumor therapeutic targets and drug combinations using synthetic lethal approaches

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 308 ◽  
Author(s):  
John S. Lazo
Keyword(s):  
Drug Targets ◽  
Gene Mutations ◽  
Cancer Therapeutics ◽  
Drug Combinations ◽  
Cancer Drug ◽  
Loss Of Function ◽  
Synthetic Lethal ◽  
Optimal Drug

Cancer drug discoverers and developers are blessed and cursed with a plethora of drug targets in the tumor cells themselves and the surrounding stromal elements. This bounty of targets has, at least in part, inspired the rapid increase in the number of clinically available small-molecule, biological, and cellular therapies for solid and hematological malignancies. Among the most challenging questions in cancer therapeutics, especially for small molecules, is how to approach loss-of-function gene mutations or deletions that encode tumor suppressors. A second mounting question is what are the optimal drug combinations. This article will briefly review the recent advances in exploiting in vitro and in vivo synthetic lethal screens to expose cancer pharmacological targets with the goal of developing new drug combinations.

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