scholarly journals The IDentif.AI 2.0 Pandemic Readiness Platform: Rapid Prioritization of Optimized COVID-19 Combination Therapy Regimens

2021 ◽  
Author(s):  
Agata Blasiak ◽  
Anh TL Truong ◽  
Alexandria Remus ◽  
Lissa Hooi ◽  
Shirley Gek Kheng Seah ◽  
...  
Keyword(s):  
Combination Therapy ◽  
Drug Efficacy ◽  
Drug Combinations ◽  
Multiple Drug ◽  
Original Strain ◽  
Live Virus ◽  
Drug Synergy ◽  
Positive Treatment ◽  
Strong Candidate ◽  
Combination Regimens

Objectives: We aimed to harness IDentif.AI 2.0, a clinically actionable AI platform to rapidly pinpoint and prioritize optimal combination therapy regimens against COVID-19. Methods: A pool of starting candidate therapies was developed in collaboration with a community of infectious disease clinicians and included EIDD-1931 (metabolite of EIDD-2801), baricitinib, ebselen, selinexor, masitinib, nafamostat mesylate, telaprevir (VX-950), SN-38 (metabolite of irinotecan), imatinib mesylate, remdesivir, lopinavir, and ritonavir. Following the initial drug pool assessment, a focused, 6-drug pool was interrogated at 3 dosing levels per drug representing nearly 10,000 possible combination regimens. IDentif.AI 2.0 paired prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus (propagated, original strain and B.1.351 variant) and Vero E6 assay with a quadratic optimization workflow. Results: Within 3 weeks, IDentif.AI 2.0 realized a list of combination regimens, ranked by efficacy, for clinical go/no-go regimen recommendations. IDentif.AI 2.0 revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived. Conclusions: IDentif.AI 2.0 rapidly revealed promising drug combinations for a clinical translation. It pinpointed dose-dependent drug synergy behavior to play a role in trial design and realizing positive treatment outcomes. IDentif.AI 2.0 represents an actionable path towards rapidly optimizing combination therapy following pandemic emergence.

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 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 Triple fixed combination for hypertension treatment — a green light in the new european Guidelines 2018

2019 ◽  
Vol 24 (6) ◽  
pp. 716-722
Author(s):  
A. O. Konradi
Keyword(s):  
Combination Therapy ◽  
Arterial Hypertension ◽  
European Society ◽  
Green Light ◽  
Drug Combinations ◽  
Fixed Dose ◽  
Multiple Drug ◽  
Triple Combination Therapy ◽  
European Guidelines ◽  
European Society Of Cardiology

The majority of patients with stable arterial hypertension require combination therapy which is supported by the clinical evidence. The established target levels of blood pressure below 130/80 mmHg are challenging and demand multiple drug combinations in a single patient. Therefore, the use of dual and triple combination therapy is getting wider, and rational triple fixed combinations are highly relevant. The updated guidelines on the diagnostics, management and treatment of arterial hypertension of the European Society of Hypertension and the European Society of Cardiology confirm and recommend early and wider use of the fixed-dose drug combinations. The paper reviews the main practical issues of the use of combination therapy, including key questions of the change from free dose to fixed dose combinations and their rational choice.

Rationale and Technique for Single and Multiple Drug Combinations in Long-term Intrathecal Infusions

Pain Practice ◽  
2001 ◽  
Vol 1 (1) ◽  
pp. 68-80 ◽  
Author(s):  
Leland Lou ◽  
Mauricio Orbegozo ◽  
Casey L. King
Keyword(s):  
Drug Combinations ◽  
Multiple Drug

scholarly journals Combination Regimens of Favipiravir Plus Interferon Alpha Inhibit Chikungunya Virus Replication in Clinically Relevant Human Cell Lines

2021 ◽  
Vol 9 (2) ◽  
pp. 307
Author(s):  
Evelyn J. Franco ◽  
Xun Tao ◽  
Kaley C. Hanrahan ◽  
Jieqiang Zhou ◽  
Jürgen B. Bulitta ◽  
...  
Keyword(s):  
Combination Therapy ◽  
Antiviral Activity ◽  
Viral Replication ◽  
Cell Lines ◽  
Interferon Alpha ◽  
Chikungunya Virus ◽  
Plaque Assay ◽  
Human Infection ◽  
Viral Burden ◽  
Combination Regimens

Chikungunya virus (CHIKV) is an alphavirus associated with a broad tissue tropism for which no antivirals or vaccines are approved. This study evaluated the antiviral potential of favipiravir (FAV), interferon-alpha (IFN), and ribavirin (RBV) against CHIKV as mono- and combination-therapy in cell lines that are clinically relevant to human infection. Cells derived from human connective tissue (HT-1080), neurons (SK-N-MC), and skin (HFF-1) were infected with CHIKV and treated with different concentrations of FAV, IFN, or RBV. Viral supernatant was sampled daily and the burden was quantified by plaque assay on Vero cells. FAV and IFN were the most effective against CHIKV on various cell lines, suppressing the viral burden at clinically achievable concentrations; although the degree of antiviral activity was heavily influenced by cell type. RBV was not effective and demonstrated substantial toxicity, indicating that it is not a feasible candidate for CHIKV. The combination of FAV and IFN was then assessed on all cell lines. Combination therapy enhanced antiviral activity in HT-1080 and SK-N-MC cells, but not in HFF-1 cells. We developed a pharmacokinetic/pharmacodynamic model that described the viral burden and inhibitory antiviral effect. Simulations from this model predicted clinically relevant concentrations of FAV plus IFN completely suppressed CHIKV replication in HT-1080 cells, and considerably slowed down the rate of viral replication in SK-N-MC cells. The model predicted substantial inhibition of viral replication by clinical IFN regimens in HFF-1 cells. Our results highlight the antiviral potential of FAV and IFN combination regimens against CHIKV in clinically relevant cell types.

Salvage immune checkpoint inhibitor (ICI) plus tyrosine kinase inhibitor (TKI) combination therapy for metastatic renal cell carcinoma (mRCC).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16567-e16567
Author(s):  
Anish B. Parikh ◽  
Sarah P. Psutka ◽  
Yuanquan Yang ◽  
Katharine Collier ◽  
Abdul Miah ◽  
...  
Keyword(s):  
Combination Therapy ◽  
Immune Checkpoint Inhibitor ◽  
Kinase Inhibitor ◽  
Objective Response ◽  
Progression Free Survival ◽  
Standard Of Care ◽  
Free Survival ◽  
Kaplan Meier ◽  
Grade 3 ◽  
Combination Regimens

e16567 Background: ICI/TKI combinations are a new standard of care for the initial treatment (tx) of mRCC. Efficacy and toxicity of such combination regimens beyond the first-line (1L) setting remain unknown. Methods: We retrospectively reviewed charts for adult patients (pts) receiving an ICI/TKI combination in any line of tx for mRCC of any histology at one of two academic centers as of May 1, 2020. ICIs included pembrolizumab (Pm), nivolumab (Ni), ipilimumab (Ip), or avelumab (Av); TKIs included sunitinib (Su), axitinib (Ax), pazopanib (Pz), lenvatinib (Ln), or cabozantinib (Ca). Clinical data including pt demographics, histology, International mRCC Database Consortium (IMDC) risk group, tx history, and ICI/TKI tx and toxicity details were recorded. Outcomes included objective response rate (ORR), median progression-free survival (mPFS), and safety, analyzed via descriptive statistics and the Kaplan-Meier method. Results: Of 85 pts, 69 (81%) were male and 67 (79%) had clear cell histology. IMDC risk was favorable (24%), intermediate (54%), poor (20%), and unknown (2%). 39% had ICI/TKI tx in the 1L setting. ICI/TKI regimens included Pm/Ax (33%), Ni/Ca (25%), Ni/Ax (20%), Av/Ax (11%), Ni/Ip/Ca (8%), Ni/Su (2%), and Ni/Ln (1%). ORR and mPFS stratified by line of tx and prior tx are shown in the table. Of 52 pts who received ICI/TKI tx as salvage (after 1L), 52% had a grade 3 or higher (≥G3) adverse event (AE), of which the most common were anorexia (13.5%), diarrhea and hypertension (11.5% each), and fatigue (9.6%). 65% of pts on salvage ICI/TKI tx stopped tx for progression/death, while 16% stopped tx for ≥G3 AE. ≥G3 AE rates by line of tx were 62.5% (2L), 50% (3L), and 45% (≥4L). Conclusions: ICI/TKI combination therapy is effective and safe beyond the 1L setting. Prior tx history appears to impact efficacy but has less of an effect on safety/tolerability. These observations will need to be confirmed in prospective studies.[Table: see text]

scholarly journals Verteporfin can Reverse the Paclitaxel Resistance Induced by YAP Over-Expression in HCT-8/T Cells without Photoactivation through Inhibiting YAP Expression

2016 ◽  
Vol 39 (2) ◽  
pp. 481-490 ◽  
Author(s):  
Wang Pan ◽  
Qian Wang ◽  
Yi Zhang ◽  
Naishu Zhang ◽  
Jiamin Qin ◽  
...  
Keyword(s):  
T Cells ◽  
Combination Therapy ◽  
Multiple Drug Resistance ◽  
Protein Detection ◽  
Multiple Drug ◽  
Over Expression ◽  
Anti Cancer ◽  
The Relationship ◽  
Proliferation And Invasion ◽  
Main Factor

Background/Aims: Paclitaxel (PTX) is one of the most effective anti-cancer drugs. However, multiple drug resistance is still the main factor that hinders the effective treatment of cancer with PTX. Several factors including YAP over-expression can cause PTX resistance. In this study, we aimed to verify the role YAP plays in PTX resistance, explore the reversal of PTX resistance by verteporfin (VP) and investigate the effect of combination therapy of PTX and VP on the PTX resistant colon cancer cells (HCT-8/T). Methods: To study the relationship between YAP and PTX resistance, a stable YAP-over-expression or YAP silencing cell line was generated by transfected with YAP-plasmids or siYAP-RNA. WST-1 assay was performed to detect the cytotoxicity of PTX on HCT-8 and HCT-8/T cells. Clone formation assay and Transwell assay was preformed to determine the cell proliferation and invasion ability respectively. Immunofluorescence and Western blot analysis was performed for protein detection. Results: YAP was stronger expressed in HCT-8/T than in HCT-8, and PTX resistance was positively correlated with the level of YAP expression. VP, a strongly YAP inhibitor, could reduce the PTX resistance on HCT-8/T cells without light activation by inhibiting YAP. Beside, VP and PTX combination therapy showed synergism on inhibition of YAP and cytotoxicity to HCT-8/T. Moreover, verteporfin and PTX combination therapy affect the invasion and colony formation ability and induce apoptosis of HCT-8/T cells. Conclusions: VP can reverse the PTX resistance induced by YAP over-expression in HCT-8/T cells without photoactivation through inhibiting YAP expression.

scholarly journals Evaluation of reverse transcriptase and protease inhibitors in two-drug combinations against human immunodeficiency virus replication.

1996 ◽  
Vol 40 (6) ◽  
pp. 1346-1351 ◽  
Author(s):  
C A Deminie ◽  
C M Bechtold ◽  
D Stock ◽  
M Alam ◽  
F Djang ◽  
...  
Keyword(s):  
Human Immunodeficiency Virus ◽  
Combination Therapy ◽  
Protease Inhibitor ◽  
Reverse Transcriptase ◽  
Protease Inhibitors ◽  
Nucleoside Analogs ◽  
Drug Combinations ◽  
Hiv Protease ◽  
Human Immunodeficiency Virus Replication ◽  
Immunodeficiency Virus

Current treatments for human immunodeficiency virus (HIV) include both reverse transcriptase and protease inhibitors. Results from in vitro and clinical studies suggest that combination therapy can be more effective than single drugs in reducing viral burden. To evaluate compounds for combination therapy, stavudine (d4T), didanosine (ddI), or BMS-186,318, an HIV protease inhibitor, were combined with other clinically relevant compounds and tested in a T-cell line (CEM-SS) that was infected with HIV-RF or in peripheral blood mononuclear cells infected with a clinical HIV isolate. The combined drug effects were analyzed by the methods described by Chou and Talalay (Adv. Enzyme Regul. 22:27-55, 1984) as well as by Prichard et al. (Antimicrob. Agents Chemother. 37:540-545, 1993). The results showed that combining two nucleoside analogs (d4T-ddI, d4T-zidovudine [AZT], and d4T-zalcitabine [ddC]), two HIV protease inhibitors (BMS-186,318-saquinavir, BMS-186,318-SC-52151, and BMS-186,318-MK-639) or a reverse transcriptase and a protease inhibitor (BMS-186,318-d4T, BMS-186,318-ddI, BMS-186,318-AZT, d4T-saquinavir, d4T-MK-639, and ddI-MK-639) yielded additive to synergistic antiviral effects. In general, analysis of data by either method gave consistent results. In addition, combined antiviral treatments involving nucleoside analogs gave slightly different outcomes in the two cell types, presumably because of a difference in phosphorylation patterns. Importantly, no strong antagonism was observed with the drug combinations studied. These data should provide useful information for the design of clinical trials of combined chemotherapy.

Predicting drug synergy for precision medicine using network biology and machine learning

2019 ◽  
Vol 17 (02) ◽  
pp. 1950012 ◽  
Author(s):  
Ali Cuvitoglu ◽  
Joseph X. Zhou ◽  
Sui Huang ◽  
Zerrin Isik
Keyword(s):  
Machine Learning ◽  
Biological Network ◽  
Network Biology ◽  
Drug Combinations ◽  
Classification Model ◽  
Molecular Networks ◽  
Collective Effects ◽  
Cancer Drug ◽  
Drug Synergy

Identification of effective drug combinations for patients is an expensive and time-consuming procedure, especially for in vitro experiments. To accelerate the synergistic drug discovery process, we present a new classification model to identify more effective anti-cancer drug pairs using in silico network biology approach. Based on the hypotheses that the drug synergy comes from the collective effects on the biological network, therefore, we developed six network biology features, including overlap and distance of drug perturbation network, that were derived by using individual drug-perturbed transcriptome profiles and the relevant biological network analysis. Using publicly available drug synergy databases and three machine-learning (ML) methods, the model was trained to discriminate the positive (synergistic) and negative (nonsynergistic) drug combinations. The proposed models were evaluated on the test cases to predict the most promising network biology feature, which is the network degree activity, i.e. the synergistic effect between drug pairs is mainly accounted by the complementary signaling pathways or molecular networks from two drugs.

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