scholarly journals Exploring the Correlation between the Cognitive Benefits of Drug Combinations in a Clinical Alzheimer Disease Database and the Efficacies of the Same Drug Combinations Predicted from a Computational Model

2018 ◽  
Author(s):  
Thomas J. Anastasio
Keyword(s):  
Computational Model ◽  
Channel Blocker ◽  
Drug Combinations ◽  
Independent Data ◽  
Antihypertensive Medications ◽  
Clinical Dataset ◽  
Cognitive Benefits ◽  
Standard Linear ◽  
Cox2 Inhibitor ◽  
Rule Out

AbstractINTRODUCTIONIdentification of drug combinations that could be effective in Alzheimer’s treatment is made difficult by the number of possible combinations. This analysis identifies as potentially therapeutic those drug combinations that rank highest when their efficacy is determined jointly from two independent data sources.METHODSEstimates of the efficacy of the same drug combinations were derived from a clinical dataset and from pre-clinical data, in the form of a computational model of neuroinflammation. Standard linear regression was used to show that the two sets of estimates were correlated, and to rule out possible confounds.RESULTSThe ten highest ranking, jointly determined drug combinations most frequently consisted of COX2 inhibitors and aspirin, along with various antihypertensive medications.DISCUSSIONTen combinations of from five to nine drugs, and the three-drug combination of a COX2 inhibitor, aspirin, and a calcium-channel blocker, are discussed as candidates for consideration in future clinical and pre-clinical studies.

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 Precision combination therapies based on recurrent oncogenic co-alterations

2020 ◽  
Author(s):  
Xubin Li ◽  
Elisabeth K. Dowling ◽  
Gonghong Yan ◽  
Behnaz Bozorgui ◽  
Parisa Imarinad ◽  
...  
Keyword(s):  
Therapeutic Benefit ◽  
Drug Combinations ◽  
Data Driven ◽  
Independent Data ◽  
Combination Therapies ◽  
Effective Combination ◽  
Resource Matching ◽  
Precision Therapy ◽  
Cancer Types ◽  
Omic Data

AbstractCancer cells depend on multiple driver alterations whose oncogenic effects can be suppressed by drug combinations. Discovery of effective combination therapies is challenging due to the complexity of the biomolecular landscape of drug responses. Here, we developed the method REFLECT (REcurrent Features Leveraged for Combination Therapies), which integrates machine learning and cancer informatics algorithms. The method maps recurrent co-alteration signatures from multi-omic data across patient cohorts to combination therapies. Using the REFLECT framework, we generated a precision therapy resource matching 2,201 drug combinations to co-alteration signatures across 201 cohorts stratified from 10,392 patients and 33 cancer types. We validated that REFLECT-predicted combinations introduce significantly higher therapeutic benefit through analysis of independent data from comprehensive drug screens. In patient cohorts with immunotherapy response markers, HER2 activation and DNA repair aberrations, we identified therapeutically actionable co-alteration signatures shared across patient sub-cohorts. REFLECT provides a framework to design combination therapies tailored to patient cohorts in data-driven clinical trials.

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.

Calcium Channel Blocker Toxicology

2005 ◽  
Vol 18 (3) ◽  
pp. 169-174 ◽  
Author(s):  
Matthew Hedge
Keyword(s):  
United States ◽  
Calcium Channel ◽  
Calcium Channel Blocker ◽  
Mechanism Of Action ◽  
Calcium Channel Blockers ◽  
Channel Blocker ◽  
Clinical Presentation ◽  
The United States ◽  
Channel Blockers ◽  
Antihypertensive Medications

Calcium channel blockers are commonly prescribed antihypertensive medications in the United States and as such are a common presenting ingestion. The pharmacology and mechanism of action of this class of drugs will be discussed. The clinical presentation and therapeutic options will be reviewed.

The use of antihypertensive medications in cancer patients on chemo immunotherapy: Protection or not?

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e19238-e19238
Author(s):  
Bohdan Baralo ◽  
Samia Hossain ◽  
Rithikaa Ellangovan ◽  
Ajinkya Kulkarni ◽  
Vinay E. Keshava ◽  
...  
Keyword(s):  
Calcium Channel ◽  
Cancer Patients ◽  
Calcium Channel Blocker ◽  
Channel Blocker ◽  
Antihypertensive Medications ◽  
Group 4 ◽  
Dihydropyridine Calcium Channel Blocker ◽  
Group 3 ◽  
Group 2 ◽  
Group 1

e19238 Background: Antihypertensive medications is a widely used in the cancer patients due to comorbid hypertension. The following analysis was devoted to find if any particular group can provide nephroprotective effect for patient that is receiving chemo immunotherapy. Methods: A retrospective cohort of the 95 patients, who received chemo immunotherapy in the infusion center of Mercy Fitzgerald Hospital in 2018-2020 were analyzed. We divided patient in 4 groups. Those who were receiving beta-blockers - group 1, angiotensin-converting enzyme/angiotensin receptor blockers – group 2, dihydropyridine calcium channel blocker – group 3, non-dihydropyridine calcium channel blocker – group 4. KDIGO criteria were used to identify patients with Acute Kidney Injury (AKI). Chi-square test was used to estimate if there is association between using specific group of anti-hypertensives medications and AKI. Results: 12 out of 95 patients in the study developed AKI. In group 1 5 had AKI and 23 not, p = .032. Group 2 included 3 patient who developed AKI and 22 who did not, p = 0.91. Group 3 group had 6 patients with AKI and 10 without, p = 0.001. Group 4 had 10 patients and none of them had AKI, p = 0.16. Conclusions: In our study we found that patient who were using the dihydropyridine calcium channel blocker (amlodipine) had a higher incidence of the AKI then patients, who were using other groups of the anti-hypertensive medications. Large prospective studies may be necessary to confirm that use of amlodipine in patients who undergo chemo immunotherapy is associated with higher incidence of the AKI and should be avoided in the treatment of this patients.

A rapid method for the direct identification of paramyxovirus in canine CSF by ultracentrifugation and negative staining as a rule out for distemper

1990 ◽  
Vol 48 (3) ◽  
pp. 594-595
Author(s):  
W.L. Steffens ◽  
M.B. Ard ◽  
C.E. Greene ◽  
A. Jaggy
Keyword(s):  
Subacute Sclerosing Panencephalitis ◽  
Clinical Signs ◽  
Viral Disease ◽  
Etiologic Agent ◽  
Mucosal Inflammation ◽  
Worldwide Distribution ◽  
Negative Stain ◽  
Viral Particles ◽  
Systemic Manifestations ◽  
Rule Out

Canine distemper is a multisystemic contagious viral disease having a worldwide distribution, a high mortality rate, and significant central neurologic system (CNS) complications. In its systemic manifestations, it is often presumptively diagnosed on the basis of clinical signs and history. Few definitive antemortem diagnostic tests exist, and most are limited to the detection of viral antigen by immunofluorescence techniques on tissues or cytologic specimens or high immunoglobulin levels in CSF (cerebrospinal fluid). Diagnosis of CNS distemper is often unreliable due to the relatively low cell count in CSF (<50 cells/μl) and the binding of blocking immunoglobulins in CSF to cell surfaces. A more reliable and definitive test might be possible utilizing direct morphologic detection of the etiologic agent. Distemper is the canine equivalent of human measles, in that both involve a closely related member of the Paramyxoviridae, both produce mucosal inflammation, and may produce CNS complications. In humans, diagnosis of measles-induced subacute sclerosing panencephalitis is through negative stain identification of whole or incomplete viral particles in patient CSF.

Impairment Questions and Answers

1999 ◽  
Vol 4 (4) ◽  
pp. 4-4
Keyword(s):  
Symptom Validity ◽  
Validity Testing ◽  
Symptom Validity Testing ◽  
Negative Results ◽  
Tunnel Vision ◽  
Questions And Answers ◽  
Blurry Vision ◽  
Fatigue Evaluation ◽  
Choice Testing ◽  
Rule Out

Abstract Symptom validity testing, also known as forced-choice testing, is a way to assess the validity of sensory and memory deficits, including tactile anesthesias, paresthesias, blindness, color blindness, tunnel vision, blurry vision, and deafness—the common feature of which is a claimed inability to perceive or remember a sensory signal. Symptom validity testing comprises two elements: A specific ability is assessed by presenting a large number of items in a multiple-choice format, and then the examinee's performance is compared with the statistical likelihood of success based on chance alone. Scoring below a norm can be explained in many different ways (eg, fatigue, evaluation anxiety, limited intelligence, and so on), but scoring below the probabilities of chance alone most likely indicates deliberate deception. The positive predictive value of the symptom validity technique likely is quite high because there is no alternative explanation to deliberate distortion when performance is below the probability of chance. The sensitivity of this technique is not likely to be good because, as with a thermometer, positive findings indicate that a problem is present, but negative results do not rule out a problem. Although a compelling conclusion is that the examinee who scores below probabilities is deliberately motivated to perform poorly, malingering must be concluded from the total clinical context.

Case Exercise: Toxic Exposure-Related Illness: Causation, Diagnosis, and Permanent Impairment

2007 ◽  
Vol 12 (2) ◽  
pp. 4-8
Author(s):  
Frederick Fung
Keyword(s):  
Concentration Level ◽  
Treatment Plan ◽  
Toxic Exposure ◽  
Dose Response Relationship ◽  
Specialty Board ◽  
History Of ◽  
Subjective Complaints ◽  
Based Medicine ◽  
Illness Causation ◽  
Rule Out

Abstract A diagnosis of toxic-related injury/illness requires a consideration of the illness related to the toxic exposure, including diagnosis, causation, and permanent impairment; these are best performed by a physician who is certified by a specialty board certified by the American Board of Preventive Medicine. The patient must have a history of symptoms consistent with the exposure and disease at issue. In order to diagnose the presence of a specific disease, the examiner must find subjective complaints that are consistent with the objective findings, and both the subjective complaints and objective findings must be consistent with the disease that is postulated. Exposure to a specific potentially causative agent at a defined concentration level must be documented and must be sufficient to induce a particular pathology in order to establish a diagnosis. Differential diagnoses must be entertained in order to rule out other potential causes, including psychological etiology. Furthermore, the identified exposure at the defined concentration level must be capable of causing the diagnosis being postulated before the examiner can conclude that there has been a cause-and-effect relationship between the exposure and the disease (dose-response relationship). The evaluator's opinion should make biological and epidemiological sense. The treatment plan and prognosis should be consistent with evidence-based medicine, and the rating of impairment must be based on objective findings in involved systems.

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