scholarly journals The search for surrogacy in patient derived xenograft mouse trials: glass is less than half full

2020 ◽  
Author(s):  
Hitesh B. Mistry
Keyword(s):  
Clinical Trials ◽  
Drug Development ◽  
Disease Progression ◽  
Treatment Effect ◽  
Patient Survival ◽  
Surrogate Marker ◽  
Tumour Burden ◽  
Patient Derived Xenograft ◽  
Clinical Community ◽  
Large Patient

AbstractDespite the efforts of many within the drug development and clinical community surrogate biomarkers for patient survival have remained elusive in Oncology. This failure in part is attributable to there being a paucity of clinical trials showing a treatment effect on patient survival. Given this issue an alternative system to explore the surrogacy potential of biomarkers are large preclinical xenograft studies i.e. panel of patient derived xenografts or mouse clinical trials. In this study we explored the surrogacy potential of tumour burden biomarkers, current size of tumour and how its changed, preclinically in a large patient derived xenograft database which contains a diverse number of drugs/treatments (n=61) and xenografts (n=245). We found that of the possible 1830 two-arm mouse trials, 1103 showed a treatment effect on the preclinical end-point, disease progression, (p<0.05). Of these only in 30% did tumour burden markers fully capture the treatment effect on disease progression times i.e. satisfied a key condition for surrogacy. These results highlight that preclinically it is very challenging to find a surrogate marker based purely on measures of tumour burden.

scholarly journals Repurposing Drugs to Treat Heart and Brain Illness

2021 ◽  
Vol 14 (6) ◽  
pp. 573
Author(s):  
Maranda S. Cantrell ◽  
Alejandro Soto-Avellaneda ◽  
Jackson D. Wall ◽  
Aaron D. Ajeti ◽  
Brad E. Morrison ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Drug Development ◽  
Neurodegenerative Diseases ◽  
Disease Progression ◽  
Drug Repurposing ◽  
Pharmacological Treatments ◽  
Representative Case ◽  
Inflammatory Processes ◽  
Expensive Process ◽  
Selection Of

Drug development is a complicated, slow and expensive process with high failure rates. One strategy to mitigate these factors is to recycle existing drugs with viable safety profiles and have gained Food and Drug Administration approval following extensive clinical trials. Cardiovascular and neurodegenerative diseases are difficult to treat, and there exist few effective therapeutics, necessitating the development of new, more efficacious drugs. Recent scientific studies have led to a mechanistic understanding of heart and brain disease progression, which has led researchers to assess myriad drugs for their potential as pharmacological treatments for these ailments. The focus of this review is to survey strategies for the selection of drug repurposing candidates and provide representative case studies where drug repurposing strategies were used to discover therapeutics for cardiovascular and neurodegenerative diseases, with a focus on anti-inflammatory processes where new drug alternatives are needed.

Exploring the validity of a synthetic control arm (SCA) for augmentation or replacement of a randomized control in difficult-to-study indications: A case study in relapsed or refractory multiple myeloma (R/R MM).

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e20521-e20521
Author(s):  
Ruthie Davi ◽  
Xiang Yin ◽  
Mark Stewart
Keyword(s):  
Clinical Trials ◽  
Drug Development ◽  
Treatment Effect ◽  
Tipping Point ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data ◽  
Randomized Control ◽  
Baseline Characteristics

e20521 Background: The randomized clinical trial (RCT) is the gold standard in drug development. However, for indications where patients have a strong preference for the investigational product, such as many oncology and rare diseases, the use of a SCA may improve drug development and reduce patient burden. SCA is an external control constructed from patient-level data from previous clinical trials to match the baseline characteristics of the patients in an investigational group and can augment a single-arm trial or a RCT compromised by control arm early withdrawal or noncompliance in order to estimate treatment effects. This research explores whether the treatment effect (difference between arms) based on an SCA can mimic the treatment effect from a RCT. Tipping point analyses were explored to assess the impact of unobserved confounders on the SCA-based demonstration of efficacy. Methods: This case study is based on patient-level data from previous clinical trials in R/R MM. The SCA patients satisfied key eligibility criteria of the target RCT and were further selected using propensity score methods to balance the baseline characteristics in the SCA with the target randomized treatment group (TRT) from the original RCT. Sensitivity analyses utilizing methods proposed by Lin (1998) illustrate the robustness of the treatment effect to unobserved covariate(s). Results: Comparable balance was achieved in observed baseline characteristics between SCA and the matched patients from TRT. The treatment effect utilizing SCA is similar to the original RCT. The Kaplan Meier curve of overall survival for the SCA overlaps with that of the randomized control and the quantified differences between SCA and the matched patients from TRT are very similar to the original RCT. Tipping point analyses show changes in HRs under representative sets of assumptions regarding the unobserved confounder (results not shown). Conclusions: This case study demonstrates an SCA built from previous clinical trials, can be well-balanced at baseline with TRT and can provide similar treatment effect estimates as a RCT. Tipping point analyses can elucidate whether treatment effects are reliable despite a reasonable degree of confounding expected in a clinical setting. This suggests, in some settings, SCA can be used to augment or replace a randomized control in future trials without loss of understanding of the treatment effect. [Table: see text]

Clinical Drug Development for the Treatment of Pulmonary Arterial Hypertension: Collaboration With the Pharmaceutical Industry and Regulatory Agencies

2010 ◽  
Vol 9 (4) ◽  
pp. 214-219
Author(s):  
Robyn J. Barst
Keyword(s):  
Clinical Trials ◽  
Pulmonary Arterial Hypertension ◽  
Drug Development ◽  
Phase 1 ◽  
Preclinical Studies ◽  
Phase 2 ◽  
Phase 3 ◽  
Preclinical Testing ◽  
New Drug ◽  
Pulmonary Arterial

Drug development is the entire process of introducing a new drug to the market. It involves drug discovery, screening, preclinical testing, an Investigational New Drug (IND) application in the US or a Clinical Trial Application (CTA) in the EU, phase 1–3 clinical trials, a New Drug Application (NDA), Food and Drug Administration (FDA) review and approval, and postapproval studies required for continuing safety evaluation. Preclinical testing assesses safety and biologic activity, phase 1 determines safety and dosage, phase 2 evaluates efficacy and side effects, and phase 3 confirms efficacy and monitors adverse effects in a larger number of patients. Postapproval studies provide additional postmarketing data. On average, it takes 15 years from preclinical studies to regulatory approval by the FDA: about 3.5–6.5 years for preclinical, 1–1.5 years for phase 1, 2 years for phase 2, 3–3.5 years for phase 3, and 1.5–2.5 years for filing the NDA and completing the FDA review process. Of approximately 5000 compounds evaluated in preclinical studies, about 5 compounds enter clinical trials, and 1 compound is approved (Tufts Center for the Study of Drug Development, 2011). Most drug development programs include approximately 35–40 phase 1 studies, 15 phase 2 studies, and 3–5 pivotal trials with more than 5000 patients enrolled. Thus, to produce safe and effective drugs in a regulated environment is a highly complex process. Against this backdrop, what is the best way to develop drugs for pulmonary arterial hypertension (PAH), an orphan disease often rapidly fatal within several years of diagnosis and in which spontaneous regression does not occur?

Intelligent Drug Development

2014 ◽  
Author(s):  
Michael Tansey
Keyword(s):  
Clinical Trials ◽  
Cost Effectiveness ◽  
Drug Development ◽  
Clinical Research ◽  
Development Process ◽  
Drug Development Process ◽  
Individual Trial ◽  
Specific Technology

Clinical research is heavily regulated and involves coordination of numerous pharmaceutical-related disciplines. Each individual trial involves contractual, regulatory, and ethics approval at each site and in each country. Clinical trials have become so complex and government requirements so stringent that researchers often approach trials too cautiously, convinced that the process is bound to be insurmountably complicated and riddled with roadblocks. A step back is needed, an objective examination of the drug development process as a whole, and recommendations made for streamlining the process at all stages. With Intelligent Drug Development, Michael Tansey systematically addresses the key elements that affect the quality, timeliness, and cost-effectiveness of the drug-development process, and identifies steps that can be adjusted and made more efficient. Tansey uses his own experiences conducting clinical trials to create a guide that provides flexible, adaptable ways of implementing the necessary processes of development. Moreover, the processes described in the book are not dependent either on a particular company structure or on any specific technology; thus, Tansey's approach can be implemented at any company, regardless of size. The book includes specific examples that illustrate some of the ways in which the principles can be applied, as well as suggestions for providing a better context in which the changes can be implemented. The protocols for drug development and clinical research have grown increasingly complex in recent years, making Intelligent Drug Development a needed examination of the pharmaceutical process.

Perspectives on Virtual (Remote) Clinical Trials as the “New Normal” to Accelerate Drug Development

2021 ◽  
Author(s):  
Demissie Alemayehu ◽  
Robert Hemmings ◽  
Kannan Natarajan ◽  
Satrajit Roychoudhury
Keyword(s):  
Clinical Trials ◽  
Drug Development ◽  
New Normal

scholarly journals Estimating and reporting treatment effects in clinical trials for weight management: using estimands to interpret effects of intercurrent events and missing data

2021 ◽  
Author(s):  
Sean Wharton ◽  
Arne Astrup ◽  
Lars Endahl ◽  
Michael E. J. Lean ◽  
Altynai Satylganova ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Weight Management ◽  
Treatment Effect ◽  
Repeated Measures ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Weight Losses ◽  
The Difference ◽  
Treatment Of Obesity

AbstractIn the approval process for new weight management therapies, regulators typically require estimates of effect size. Usually, as with other drug evaluations, the placebo-adjusted treatment effect (i.e., the difference between weight losses with pharmacotherapy and placebo, when given as an adjunct to lifestyle intervention) is provided from data in randomized clinical trials (RCTs). At first glance, this may seem appropriate and straightforward. However, weight loss is not a simple direct drug effect, but is also mediated by other factors such as changes in diet and physical activity. Interpreting observed differences between treatment arms in weight management RCTs can be challenging; intercurrent events that occur after treatment initiation may affect the interpretation of results at the end of treatment. Utilizing estimands helps to address these uncertainties and improve transparency in clinical trial reporting by better matching the treatment-effect estimates to the scientific and/or clinical questions of interest. Estimands aim to provide an indication of trial outcomes that might be expected in the same patients under different conditions. This article reviews how intercurrent events during weight management trials can influence placebo-adjusted treatment effects, depending on how they are accounted for and how missing data are handled. The most appropriate method for statistical analysis is also discussed, including assessment of the last observation carried forward approach, and more recent methods, such as multiple imputation and mixed models for repeated measures. The use of each of these approaches, and that of estimands, is discussed in the context of the SCALE phase 3a and 3b RCTs evaluating the effect of liraglutide 3.0 mg for the treatment of obesity.

scholarly journals Competing risk and heterogeneity of treatment effect in clinical trials

Trials ◽  
2008 ◽  
Vol 9 (1) ◽  
Author(s):  
David M Kent ◽  
Alawi Alsheikh-Ali ◽  
Rodney A Hayward
Keyword(s):  
Clinical Trials ◽  
Treatment Effect ◽  
Competing Risk ◽  
Heterogeneity Of Treatment Effect
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