scholarly journals A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Joseph M. Cicchese ◽  
Awanti Sambarey ◽  
Denise Kirschner ◽  
Jennifer J. Linderman ◽  
Sriram Chandrasekaran
Keyword(s):  
Drug Interactions ◽  
Scale Model ◽  
Relative Distribution ◽  
Combination Therapies ◽  
Multi Scale ◽  
Machine Learning Model ◽  
Drug Regimens ◽  
New Treatments

AbstractTuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim. We calculate an in vivo drug interaction score (iDIS) from dynamics of drug diffusion, spatial distribution, and activity within lesions against various pathogen sub-populations. The iDIS of drug regimens evaluated against non-replicating bacteria significantly correlates with efficacy metrics from clinical trials. Our approach identifies mechanisms that can amplify synergistic or mitigate antagonistic drug interactions in vivo by modulating the relative distribution of drugs. Our mechanistic framework enables efficient evaluation of in vivo drug interactions and optimization of combination therapies.

scholarly journals A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs

2020 ◽  
Author(s):  
Joseph Cicchese ◽  
Awanti Sambarey ◽  
Denise Kirschner ◽  
Jennifer Linderman ◽  
Sriram Chandrasekaran
Keyword(s):  
Drug Interactions ◽  
Scale Model ◽  
Relative Distribution ◽  
Combination Therapies ◽  
Multi Scale ◽  
Machine Learning Model ◽  
Drug Regimens ◽  
New Treatments

AbstractTuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim. We calculate an in vivo drug interaction score (iDIS) from dynamics of drug diffusion, spatial distribution, and activity within lesions against various pathogen sub-populations. The iDIS of drug regimens evaluated against non-replicating bacteria significantly correlates with efficacy metrics from clinical trials. Our approach identifies mechanisms that can amplify synergistic or mitigate antagonistic drug interactions in vivo by modulating the relative distribution of drugs. Our mechanistic framework enables efficient evaluation of in vivo drug interactions and optimization of combination therapies.

scholarly journals A multi-scale model of the yeast chromosome-segregation system

2018 ◽  
Author(s):  
Cai Tong Ng ◽  
Li Deng ◽  
Chen Chen ◽  
Hong Hwa Lim ◽  
Jian Shi ◽  
...  
Keyword(s):  
Chromosome Segregation ◽  
Scale Model ◽  
Microtubule Depolymerization ◽  
Multi Scale ◽  
Yeast Chromosome ◽  
Dividing Cells ◽  
Electron Cryotomography ◽  
Binding Position

ABSTRACTIn dividing cells, depolymerizing spindle microtubules move chromosomes by pulling at their kinetochores. While kinetochore subcomplexes have been studied extensively in vitro, little is known about their in vivo structure and interactions with microtubules or their response to spindle damage. Here we combine electron cryotomography of serial cryosections with genetic and pharmacological perturbation to study the yeast chromosome-segregation machinery at molecular resolution in vivo. Each kinetochore microtubule has one (rarely, two) Dam1C/DASH outer-kinetochore assemblies.Dam1C/DASH only contacts the flat surface of the microtubule and does so with its flexible “bridges”. In metaphase, 40% of the Dam1C/DASH assemblies are complete rings; the rest are partial rings. Ring completeness and binding position along the microtubule are sensitive to kinetochore attachment and tension, respectively. Our study supports a model in which each kinetochore must undergo cycles of conformational change to couple microtubule depolymerization to chromosome movement.

scholarly journals Hierarchy of evidence in interpretation of clinical significance of drug interactions

2012 ◽  
Vol 65 (1-2) ◽  
pp. 45-49
Author(s):  
Bozana Nikolic ◽  
Miroslav Savic
Keyword(s):  
Drug Metabolism ◽  
Drug Interactions ◽  
Clinical Significance ◽  
Health Professionals ◽  
Molecular Entity ◽  
Population Pharmacokinetic ◽  
Pharmacokinetic Studies ◽  
Potential Interactions

Introduction. Since drug interactions may result in serious adverse effects or failure of therapy, it is of huge importance that health professionals base their decisions about drug prescription, dispensing and administration on reliable research evidence, taking into account the hierarchy of data sources for evaluation. Clinical Significance of Potential Interactions - Information Sources. The sources of data regarding drug interactions are numerous, beginning with various drug reference books. However, they are far from uniformity in the way of choosing and presenting putative clinically relevant interactions. Clinical Significance of Potential Interactions - Interpretation of Information. The difficulties in interpretation of drug interactions are illustrated through the analysis of a published example involving assessment made by two different groups of health professionals. Systematic Evaluation of Drug-Drug Interaction. The potential for interactions is mainly investigated before marketing a drug. Generally, the in vitro, followed by in vivo studies are to be performed. The major metabolic pathways involved in the metabolism of a new molecular entity, as well as the potential of induction of human enzymes involved in drug metabolism are to be examined. In the field of interaction research it is possible to make use of the population pharmacokinetic studies as well as of the pharmacodynamic assessment, and also the postregistration monitoring of the reported adverse reactions and other literature data. Conclusion. In vitro and in vivo drug metabolism and transport studies should be conducted to elucidate the mechanisms and potential for drug-drug interactions. The assessment of their clinical significance should be based on well-defined and validated exposure-response data.

The Use of Lectins as Tools to Combat SARS-CoV-2

2021 ◽  
Vol 27 ◽  
Author(s):  
Daniela Martinez ◽  
Diego Amaral ◽  
David Markovitz ◽  
Luciano Pinto
Keyword(s):  
Viral Infection ◽  
Host Cells ◽  
Viral Fusion ◽  
Cell Receptors ◽  
First Case ◽  
Mannose Binding ◽  
New Treatments ◽  
Viral Surface

Background: in december 2019, china announced the first case of an infection caused by an, until then, unknown virus: sars-cov-2. since then, researchers have been looking for viable alternatives for the treatment and/or cure of viral infection. among the possible complementary solutions are lectins, and proteins that are reversibly bound to different carbohydrates. the spike protein, present on the viral surface, can interact with different cell receptors: ace2, cd147, and dc-signr. since lectins have an affinity for different carbohydrates, the binding with the glycosylated cell receptors represents a possibility of preventing the virus from binding to the receptors of host cells. Objective: in this review we discuss the main lectins that are possible candidates for use in the treatment of covid-19, highlighting those that have already demonstrated antiviral activity in vivo and in vitro, including mannose-binding lectin, griffithsin, banlec, and others. we also aim to discuss the possible mechanism of action of lectins, which appears to occur through the mediation of viral fusion in host cells, by binding of lectins to glycosylated receptors found in human cells and/or binding of these proteins with the spike glycoprotein, present in virus surface.moreover, we also discuss the use of lectins in clinical practice. Conclusion: Even with the development of effective vaccines, new cases of viral infection with the same virus, or new outbreaks with different viruses can occur; so, the development of new treatments should not be discarded. moreover, the discussions made in this work are relevant regarding the anti-viral properties of lectins.

Oral Terbinafine: A New Antifungal Agent

1997 ◽  
Vol 31 (4) ◽  
pp. 445-456 ◽  
Author(s):  
Susan M Abdel-Rahman ◽  
Milap C Nahata
Keyword(s):  
Adverse Effects ◽  
Drug Interactions ◽  
Fungal Infections ◽  
Case Reports ◽  
Current Standard ◽  
Open Label ◽  
Double Blind ◽  
Pathogenic Yeast

Objective To review the pharmacology, pharmacokinetics, efficacy, adverse effects, drug interactions, and dosage guidelines of terbinafine. Available comparative data of terbinafine and other antimycotic agents are described for understanding the potential role of terbinafine in patient care. Data Sources A MEDLINE search restricted to English language during 1966–1996 and extensive review of journals was conducted to prepare this article. MeSH headings included allylamines, terbinafine, SF 86–327, dermatophytosis, dermatomycosis. Data Extraction The data on pharmacokinetics, adverse effects, and drug interactions were obtained from open-label and controlled studies and case reports. Controlled single- or double-blind studies were evaluated to describe the efficacy of terbinafine in the treatment of various fungal infections. Data Synthesis Terbinafine is the first oral antimycotic in the allylamines class: a fungicidal agent that inhibits ergosterol synthesis at the stage of squalene epoxidation. Terbinafine demonstrates excellent in vitro activity against the majority of dermatophyte species including Trichophyton rubrum, Trichophyton mentagrophytes, and Epidermophyton floccosum; less activity is seen against Dematiaceae and the filamentous fungi. It is least active against the pathogenic yeast and this correlates with the relatively poor efficacy against these organisms in vivo. High concentrations of terbinafine are achieved in keratinous tissues, the site of superficial infections, and these concentrations are maintained for up to 3 months. The clinical efficacy of terbinafine against a number of dermatophyte infections exceeds that of the current standard of therapy, griseofulvin. The efficacy of terbinafine may be as good or better than that of the azole antifungals. Additional studies are required to confirm these observations. Terbinafine demonstrates a good safety profile, and relatively few drug interactions have been identified. Conclusions Terbinafine is more effective than the gold standard, griseofulvin, in the treatment of tinea pedis and tinea unguinum, with considerably shorter treatment duration in the latter. It has been proven as effective as griseofulvin in the treatment of tinea capitis, tinea corporis, and tinea cruris. Terbinafine does not appear to offer any advantage in the treatment of nondermatophyte infections; its utility in the treatment of systemic infections has yet to be established. Depending on individual institutional costs, terbinafine may be a front-line drug for some superficial infections responding poorly to the current standard of therapy.

In Vitro/In Vivo Correlation for Drug–Drug Interactions

2011 ◽  
pp. 133-160 ◽  
Author(s):  
Xavier Boulenc ◽  
Wolfgang Schmider ◽  
Olivier Barberan
Keyword(s):  
Drug Interactions

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.

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