scholarly journals Translational Medicine and Drug Discovery: A Mini Review

2018 ◽  
Vol III (I) ◽  
pp. 6-15
Author(s):  
Muhammad Saadullah Khan ◽  
Farwa Shahid ◽  
Arooj Shahid ◽  
Gul Shahnaz

There are a very large number of drugs that enter in the clinical trial phase but only a fraction of them is able to get their place in market. For a drug to reach at a phase of clinical trial requires a huge effort of research and a very large investment. Translational medicine, a relatively new discipline, uses the novel techniques that not only lower the risk of investment failure, but also focuses on reducing the testing duration in different phases of clinical trials. Discussed in the article are advantages of translational medicines and various challenges faced by translational medicine as well the ways by which this discipline will face these challenges. This article also focuses on recent advances in therapeutic development for diabetes, bone disorders, neurosciences, and oncology and the failures of translational medicine due to high external risk factors.

1999 ◽  
Author(s):  
Hermano I. Krebs ◽  
Neville Hogan ◽  
Bruce Volpe ◽  
Mindy Aisen ◽  
Lisa Edelstein ◽  
...  

Abstract We are applying robotics and information technology to assist, enhance, and quantify neuro-rehabilitation. Our goal is a new class of interactive, user-affectionate clinical devices designed not only for evaluating patients, but also for delivering meaningful therapy via engaging “video games.” Notably, the novel robot MIT-MANUS has been designed and programmed for clinical neurological applications, and has undergone extensive clinical trials for more than four years at Burke Rehabilitation Hospital – White Plains, NY. This paper will review the main result of the pilot clinical trial of the 20 patients focusing on the consequences of thalamic lesions.


Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 4534-4534 ◽  
Author(s):  
Giuseppe Saglio ◽  
Philipp le Coutre ◽  
Jorge E. Cortes ◽  
Jiří Mayer ◽  
Philip A. Rowlings ◽  
...  

Abstract Background: The safety profile of each BCR-ABL1– targeted tyrosine kinase inhibitor (TKI) used to treat chronic myeloid leukemia (CML) is unique and should be considered when choosing therapy. Although rare, potentially severe adverse events have been reported in CML pts treated with TKIs, particularly pulmonary arterial hypertension with DAS (Montani, Circulation 2012), peripheral arterial occlusive disease with nilotinib (Kim, Leukemia 2013), and arterial and venous occlusive events with ponatinib (Cortes, N Engl J Med 2013). The incidence of CV ischemic events in DAS-treated pts from clinical trials was assessed. Standardized incidence rates (SIRs) were calculated to determine if the number of observed events was different than expected compared with reference populations. Methods: Incidence of CV ischemic events (Table 1) were assessed in a pooled population of DAS-treated pts with any phase CML or Philadelphia chromosome-positive (Ph+) acute lymphoblastic leukemia from 11 first- and second-line clinical trials (N=2712; median age, 54 years [y]), newly diagnosed CML pts treated with DAS (n=258; median age, 46 y) or imatinib (IM; n=258; median age, 49 y) from the phase 3 DASISION trial only (CA180-056, NCT 00481247), and prostate cancer pts from the phase 3 READY trial treated with DAS or placebo in combination with docetaxel and prednisone (CA180-227, NCT 00744497; N=1518; about 66% of pts were >65 y of age). Reference populations for SIR analyses were a general population (total N=116,000,000; males N=56,000,000), CML pts (N=16,000), and prostate cancer pts (N=530,000) derived from Truven's MarketScan Commercial Claims and Medicare Supplemental database, 2008–2013, narrowed to mimic clinical trial eligibility. SIRs were calculated by dividing the observed number of events in DAS-treated pts by the expected number of events, based on the DAS exposure and reference population event rates. Results: Within the pooled population, 96 pts (4%) had CV ischemic events. In DASISION, 10 DAS- and 4 IM-treated pts had any grade CV ischemic events. In READY, 18 pts in the DAS arm and 9 pts in the placebo arm had any grade CV ischemic events. The majority of pts with a CV event had a history and/or risk factors for atherosclerosis (77/96 [80%] in the pooled population; 8/10 with DAS and 3/4 with IM in DASISION). Time-to-event analysis revealed that, in the pooled population, CV ischemic events occurred in 69/96 pts (72%) within 1 y, 11 pts (11%) in 1–2 y, and 16 pts (17%) in 3–7 y. Over 70% tolerated continued DAS therapy without a recurrent CV event. In DASISION, CV events occurred in 7/10 pts within 1 y of DAS initiation, 2 pts in 1–3 y, and 1 pt after 5 y. Based on SIRs, the observed number of CV events in DAS-treated pts was not higher than expected, given the rates of reference populations (Table 1). SIR results should be interpreted with caution due to the limitations of the indirect comparison between clinical data and claims data (eg, coding differences and surveillance bias). Conclusion: CV ischemic events were reported in 4% of DAS- and 2% of IM-treated pts in DASISION, 4% of DAS-treated pts in the pooled population, and 2% of DAS- and 1% of placebo-treated pts in READY. In all populations, among pts who experienced an event, the majority had a history of arterial ischemic events and/or risk factors for atherosclerosis, and most events occurred early. SIRs suggest that the total number of CV ischemic events among DAS-treated pts was not higher than expected, and in contrast to what has been observed with other TKIs, largely restricted to 1 y after initiating therapy. Disclosures Saglio: Pfizer: Consultancy, Fees for occasional speeches, Fees for occasional speeches Other; Novartis: Consultancy, Fees for occasional speeches, Fees for occasional speeches Other; ARIAD: Consultancy, Fees for occasional speeches, Fees for occasional speeches Other; BMS: Consultancy, Fees for occasional speeches Other. Off Label Use: Dasatinib is approved for first line use in adults with chronic phase Ph+ CML, and in adult AP- or BP-CML, and Ph+ ALL patients who are resistant or intolerant to prior therapy. le Coutre:ARIAD: Honoraria; Pfizer: Honoraria; Bristol-Myers Squibb: Honoraria; Novartis: Honoraria. Cortes:Teva: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Ariad: Consultancy, Research Funding. Mayer:Novartis: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding. Mahon:ARIAD: Consultancy, Research Funding; Pfizer : Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding. Kroog:Bristol-Myers Squibb: Employment. Gooden:Bristol Myers Squibb: Employment. Subar:Bristol Myers Squibb: Employment. Preston:Bristol-Myers Squibb: Employment. Shah:ARIAD: Research Funding; Bristol-Myers Squibb: Research Funding.


2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 5559-5559
Author(s):  
Bradley Corr ◽  
Marisa Moroney ◽  
Jeanelle Sheeder ◽  
Brandon Sawyer ◽  
S. Gail Eckhardt ◽  
...  

5559 Background: Ovarian cancer patients who enroll in Phase I clinical trials are typically platinum resistant, heavily pretreated patients with a poor prognosis. Historically, clinical benefit of Phase I trials in this patient population has been uncertain. We assessed prognostic factors and survival in women with recurrent, previously treated ovarian cancer who enrolled in Phase I clinical trials. Methods: We performed a retrospective analysis of all ovarian cancer patients who were treated on Phase I clinical trials from 2008 through 2018 at the University of Colorado Cancer Center. Patient characteristics, treatment-related toxicities and survival data were assessed. Descriptive statistics and Cox proportional hazards models were utilized to identify risk factors associated with survival time. Results: A total of 132 individual patients were treated on Phase I clinical trials. Patients had a median age of 59 years (range 33-88) with a median of 5.5 (range 1-13) previous chemotherapy lines. 53/132 (40%) of patients were treated on multiple Phase I trials with a median of 1 (range 0-5) prior Phase 1 clinical trial enrollments. All patients had an ECOG performance status of 0 or 1. Overall response rate (defined as complete or partial response) was 9% and disease control rate (defined as complete or partial response or stable disease as best response) was 33%. Median overall survival (OS) was 11.5 months (95% CI: 9.3-13.7). Two patients died on trial due to progression of disease while no patients died due to treatment-related toxicity. In multivariate analysis, independent risk factors predicting shorter survival were elevated CA-125 (HR 2.8; 95% CI: 1.6-5.2) and albumin < 3.5 g/dL (HR 2.5; 95% CI: 1.65-3.79). BMI > 25 predicted longer survival (HR 0.65; 95% CI: 0.44-0.96). Conclusions: Phase I clinical trials for heavily pretreated ovarian cancer patients are safe by a standard of no patients experiencing toxicity-related deaths in our study. They are clinically efficacious with patients experiencing OS of 11.5 months, which is comparable to existing approved therapies. Elevated CA-125 and low albumin levels predict shorter survival, while BMI > 25 predicts longer survival. Phase I clinical trial options should be considered for all heavily pretreated ovarian cancer patients if available to them.


2018 ◽  
Vol 34 (3) ◽  
pp. 123-133 ◽  
Author(s):  
Grazia Murphy ◽  
Yasmin Grace ◽  
Sadaf Chaudry ◽  
Rita Chamoun

Objective: To evaluate the efficacy, safety, and clinical implication of betrixaban for prophylaxis of venous thromboembolism (VTE) in patients with acute medical illness. Data Sources: A search for clinical trials was performed from January 2006 to January 2017 in English language using Clinicaltrials.gov and PubMed/MEDLINE. The following search terms were used: betrixaban, pharmacokinetics, pharmacology, and drug safety. Study Selection: The following limits were used to access the clinical trials: controlled clinical trial, randomized clinical trial, clinical trial, clinical trial phase II, and clinical trial phase III. The search was narrowed to include only humans. Data Extraction: The search criteria resulted in 6 clinical trials assessing the safety and efficacy of betrixaban. Additionally, references from publications assessing the safety and efficacy of betrixaban in atrial fibrillation, treatment and prevention of VTE, and extended duration VTE prophylaxis were assessed. Data Synthesis: Prior to 2017, no anticoagulant therapy had been approved for extended VTE prophylaxis in acutely ill medical patients. Betrixaban is the first direct oral anticoagulant approved for VTE prophylaxis in adult, acutely ill patients at risk for thromboembolisms. Based on the APEX trial, betrixaban 80 mg administered daily for 35 to 42 days was compared to enoxaparin administered daily for 6 to 14 days. In 7441 patients, fewer VTEs were seen in the betrixaban compared to enoxaparin with no significant difference in adverse reactions. Conclusion: Based on clinical trials, betrixaban appears to be safe and effective in preventing VTE in acutely ill patients who are at risk of developing VTE.


2017 ◽  
Author(s):  
Andrew D. Rouillard ◽  
Mark R. Hurle ◽  
Pankaj Agarwal

ABSTRACTTarget selection is the first and pivotal step in drug discovery. An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent. We collected a set of 332 targets that succeeded or failed in phase III clinical trials, and explored whether Omic features describing the target genes could predict clinical success. We obtained features from the recently published comprehensive resource: Harmonizome. Nineteen features appeared to be significantly correlated with phase III clinical trial outcomes, but only 4 passed validation schemes that used bootstrapping or modified permutation tests to assess feature robustness and generalizability while accounting for target class selection bias. We also used classifiers to perform multivariate feature selection and found that classifiers with a single feature performed as well in cross-validation as classifiers with more features (AUROC=0.57 and AUPR=0.81). The two predominantly selected features were mean mRNA expression across tissues and standard deviation of expression across tissues, where successful targets tended to have lower mean expression and higher expression variance than failed targets. This finding supports the conventional wisdom that it is favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Overall, our results suggest that it is feasible to construct a model integrating interpretable target features to inform target selection. We anticipate deeper insights and better models in the future, as researchers can reuse the data we have provided to improve methods for handling sample biases and learn more informative features. Code, documentation, and data for this study have been deposited on GitHub athttps://github.com/arouillard/omic-features-successful-targets.AUTHOR SUMMARYDrug discovery often begins with a hypothesis that changing the abundance or activity of a target—a biological molecule, usually a protein—will cure a disease or ameliorate its symptoms. Whether a target hypothesis translates into a successful therapy depends in part on the characteristics of the target, but it is not completely understood which target characteristics are important for success. We sought to answer this question with a supervised machine learning approach. We obtained outcomes of target hypotheses tested in clinical trials, scoring targets as successful or failed, and then obtained thousands of features (i.e. properties or characteristics) of targets from dozens of biological datasets. We statistically tested which features differed between successful and failed targets, and built a computational model that used these features to predict success or failure of targets in clinical trials. We found that successful targets tended to have more variable mRNA abundance from tissue to tissue and lower average abundance across tissues than failed targets. Thus, it is probably favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Our work demonstrates the feasibility of predicting clinical trial outcomes from target features.


2021 ◽  
pp. jnnp-2021-326569
Author(s):  
Robin Brown ◽  
Audrey Low ◽  
Hugh S Markus

BackgroundWhite matter hyperintensities (WMHs) are a highly prevalent MRI marker of cerebral small vessel disease (SVD), which predict stroke and dementia risk, and are being increasingly used as a surrogate marker in clinical trials. However, the influence of study population selection on WMH progression rate has not been studied and the effect of individual patient factors for WMH growth are not fully understood.MethodsWe performed a systematic review and meta-analysis of the literature on progression of WMHs in longitudinal studies to determine rates of WMH growth, and how these varied according to population characteristics and cardiovascular risk factors. We used these data to calculate necessary sample sizes for clinical trials using WMH as an endpoint.ResultsWMH growth rate was highest in SVD (2.50cc/year), intermediate in unselected stroke patients (1.29cc/year) and lower in patients with non-stroke cardiovascular disease, and with cognitive impairment. Age was significantly associated with progression (correlation coefficient 0.15cc/year, 95% CI 0.02 to 0.28cc/year) as was baseline lesion volume (0.6cc/year, 95% CI 0.13 to 1.06 cc/year). Both hypertension (OR 1.72, 95% CI 1.19 to 2.46) and current smoking (OR 1.48, 95% CI 1.02 to 2.16) were associated with WMH growth. Sample sizes for a clinical trial varied greatly with patient population selection and baseline lesion volume; estimates are provided.ConclusionsWMH progression varies markedly according to the characteristics of the population being studied and this will have a major impact on sample sizes required in a clinical trial. Our sample size estimates provide data for planning clinical trials using WMH as an outcome measure.PROSPERO registration numberCRD42020191781.


2020 ◽  
Author(s):  
Jasper Kyle Catapang ◽  
Junie B. Billones

SARS-CoV-2 has no known vaccine nor any effective treatment that has been released for clinical trials yet. This has ultimately paved the way for novel drug discovery approaches since although there are multiple efforts focused on drug repurposing of clinically-approved drugs for SARS-CoV-2, it is also worth considering that these existing drugs can be surpassed in effectivity by novel ones. This research focuses on the generation of novel candidate inhibitors via constrained graph variational autoencoders and the calculation of their Tanimoto similarities against existing drugs---repurposing these existing drugs and considering the novel ligands as possible SARS-CoV-2 main protease inhibitors and ACE2 receptor blockers by docking them through PyRx and ranking these ligands.


2004 ◽  
Vol 22 (3) ◽  
pp. 454-463 ◽  
Author(s):  
Robert J. Motzer ◽  
Jennifer Bacik ◽  
Lawrence H. Schwartz ◽  
Victor Reuter ◽  
Paul Russo ◽  
...  

Purpose To describe survival in previously treated patients with metastatic renal cell carcinoma (RCC) who are candidates for clinical trials of new agents as second-line therapy. Patients and Methods The relationship between pretreatment clinical features and survival was studied in 251 patients with advanced RCC treated during 29 consecutive clinical trials between 1975 and 2002. Clinical features were first examined in univariate analyses, and then a stepwise modeling approach based on Cox regression was used to form a multivariate model. Results Median survival for the 251 patients was 10.2 months and differed according to year of treatment, with patients treated after 1990 showing longer survival. In this group, the median overall survival time was 12.7 months. Because the purpose of this analysis was to establish prognostic factors for present-day clinical trial design, prognostic factor analysis was performed on these patients. Pretreatment features associated with a shorter survival in the multivariate analysis were low Karnofsky performance status, low hemoglobin level, and high corrected serum calcium. These were used as risk factors to categorize patients into three different groups. The median time to death in patients with zero risk factors was 22 months. The median survival in patients with one of these prognostic factors was 11.9 months. Patients with two or three risk factors had a median survival of 5.4 months. Conclusion Treatment with novel agents during a clinical trial is indicated for patients with metastatic RCC after progression to cytokine treatment. Three prognostic factors for predicting survival were used to categorize patients into risk groups. These risk categories can be used in clinical trial design and interpretation.


2020 ◽  
Author(s):  
Jasper Kyle Catapang ◽  
Junie B. Billones

SARS-CoV-2 has no known vaccine nor any effective treatment that has been released for clinical trials yet. This has ultimately paved the way for novel drug discovery approaches since although there are multiple efforts focused on drug repurposing of clinically-approved drugs for SARS-CoV-2, it is also worth considering that these existing drugs can be surpassed in effectivity by novel ones. This research focuses on the generation of novel candidate inhibitors via constrained graph variational autoencoders and the calculation of their Tanimoto similarities against existing drugs---repurposing these existing drugs and considering the novel ligands as possible SARS-CoV-2 main protease inhibitors and ACE2 receptor blockers by docking them through PyRx and ranking these ligands. Additionally, this research has successfully generated three novel ligands for the SARS-CoV-2 main protease and four novel ligands for the ACE2 receptor.


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