scholarly journals Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination

2021 ◽  
Vol 3 ◽  
Author(s):  
Dimitrios Kiagias ◽  
Giulia Russo ◽  
Giuseppe Sgroi ◽  
Francesco Pappalardo ◽  
Miguel A. Juárez
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Posterior Distribution ◽  
In Silico ◽  
Therapeutic Vaccination ◽  
Virtual Patients ◽  
Bayesian Hierarchical ◽  
Bayesian Hierarchical Method ◽  
Joint Posterior Distribution

We propose a Bayesian hierarchical method for combining in silico and in vivo data onto an augmented clinical trial with binary end points. The joint posterior distribution from the in silico experiment is treated as a prior, weighted by a measure of compatibility of the shared characteristics with the in vivo data. We also formalise the contribution and impact of in silico information in the augmented trial. We illustrate our approach to inference with in silico data from the UISS-TB simulator, a bespoke simulator of virtual patients with tuberculosis infection, and synthetic physical patients from a clinical trial.

scholarly journals In silico imaging clinical trials: cheaper, faster, better, safer, and more scalable

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Aldo Badano
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Medical Devices ◽  
In Silico ◽  
Lessons Learned ◽  
Patient Access ◽  
High Quality ◽  
Virtual Imaging ◽  
Regulatory Evaluation

AbstractImaging clinical trials can be burdensome and often delay patient access to novel, high-quality medical devices. Tools for in silico imaging trials have significantly improved in sophistication and availability. Here, I describe some of the principal advantages of in silico imaging trials and enumerate five lessons learned during the design and execution of the first all-in silico virtual imaging clinical trial for regulatory evaluation (the VICTRE study).

scholarly journals Two-stage Bayesian hierarchical modeling for blinded and unblinded safety monitoring in randomized clinical trials

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Junhao Liu ◽  
Jo Wick ◽  
Renee’ H. Martin ◽  
Caitlyn Meinzer ◽  
Dooti Roy ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Drug Safety ◽  
Safety Data ◽  
Safety Monitoring ◽  
Safety Signal ◽  
Two Stage ◽  
Bayesian Hierarchical ◽  
Potential Safety ◽  
Stage 1

Abstract Background Monitoring and reporting of drug safety during a clinical trial is essential to its success. More recent attention to drug safety has encouraged statistical methods development for monitoring and detecting potential safety signals. This paper investigates the potential impact of the process of the blinded investigator identifying a potential safety signal, which should be further investigated by the Data and Safety Monitoring Board with an unblinded safety data analysis. Methods In this paper, two-stage Bayesian hierarchical models are proposed for safety signal detection following a pre-specified set of interim analyses that are applied to efficacy. At stage 1, a hierarchical blinded model uses blinded safety data to detect a potential safety signal and at stage 2, a hierarchical logistic model is applied to confirm the signal with unblinded safety data. Results Any interim safety monitoring analysis is usually scheduled via negotiation between the trial sponsor and the Data and Safety Monitoring Board. The proposed safety monitoring process starts once 53 subjects have been enrolled into an eight-arm phase II clinical trial for the first interim analysis. Operating characteristics describing the performance of this proposed workflow are investigated using simulations based on the different scenarios. Conclusions The two-stage Bayesian safety procedure in this paper provides a statistical view to monitor safety during the clinical trials. The proposed two-stage monitoring model has an excellent accuracy of detecting and flagging a potential safety signal at stage 1, and with the most important feature that further action at stage 2 could confirm the safety issue.

scholarly journals Alzheimer’s Disease Neuropathological Comorbidities Are Common in the Younger-Old

2020 ◽  
Author(s):  
Thomas G. Beach ◽  
Michael Malek-Ahmadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Clinical Response ◽  
Oldest Old ◽  
Diagnostic Methods ◽  
Lower Probability ◽  
Molecular Therapeutics

AbstractClinicopathological studies have demonstrated that Alzheimer’s disease dementia (ADD) is often accompanied by clinically undetectable comorbid neurodegenerative and cerebrovascular disease that alter the presence and rate of cognitive decline in aging and ADD. Aside from causing increased variability in clinical response, it is possible that the major ADD comorbidities may not respond to ADD-specific molecular therapeutics. As most reports have focused on comorbidity in the oldest-old, its extent in younger age groups that are more likely to be involved in clinical trials is largely unknown. We conducted a survey of neuropathological comorbidities in sporadic ADD using data from the US National Alzheimer’s Coordinating Center. Subject data was restricted to those with dementia and meeting National Institute on Aging-Alzheimer’s Association (NIA-AA) intermediate or high AD Neuropathological Change (ADNC) levels, excluding those with known autosomal dominant AD-related mutations. Subjects were divided into age-at-death categories for analysis: under 60, 60-69, 70-79, 80-89, 90-99 and 100 or over. Confirmatory of earlier reports, ADD histopathology is less severe with advancing age, effectively increasing the relative contribution of comorbidities, most of which rise in prevalence with age. Highly prevalent ADD comorbidities are not restricted to the oldest-old but are common even in early-onset ADD. The percentage of cases with ADD as the sole major neuropathological diagnosis is highest in the under-60 group, where “pure” ADD cases are still in the minority at 44%. After this AD as a sole major pathology in ADD declines to roughly 20% in the 70s and beyond. Comorbidity rates for some pathologies, especially LBD, are high even in subjects in their 60s and 70s, at nearly 60%, but for most others, their prevalence increases with age. TDP-43 pathology affects more than 35% of ADD subjects 80 and over while microscopic infarcts reach this rate a decade later. Gross infarcts rise more slowly and affect fewer subjects but still involve 15-20% of ADD after age 80. White matter rarefaction may be underestimated in the NACC database but is present in almost 70% of centenarians with ADD. Effective clinical trials depend on accurate estimates of required subject numbers, which are dependent on observed effect size and clinical response variability. Comorbidities are likely to affect both, leading to lower probability of clinical trial success. Stratifying ADD clinical trial analyses by presence and types of accompanying comorbidities might identify subgroups with higher effect sizes and greater clinical response rates, but accurate in-vivo diagnostic methods for most comorbidities are still lacking.

scholarly journals LSD1 as a Biomarker and the Outcome of Its Inhibitors in the Clinical Trial: The Therapy Opportunity in Tumor

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Clement Agboyibor ◽  
Jianshu Dong ◽  
Clement Yaw Effah ◽  
Emmanuel Kwateng Drokow ◽  
Waqar Pervaiz ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Clinical Results ◽  
Tumor Patients ◽  
Therapy Targets ◽  
Dosing Regimens ◽  
Novel Biomarkers ◽  
Selection Of

Tumors are the foremost cause of death worldwide. As a result of that, there has been a significant enhancement in the investigation, treatment methods, and good maintenance practices on cancer. However, the sensitivity and specificity of a lot of tumor biomarkers are not adequate. Hence, it is of inordinate significance to ascertain novel biomarkers to forecast the prognosis and therapy targets for tumors. This review characterized LSD1 as a biomarker in different tumors. LSD1 inhibitors in clinical trials were also discussed. The recent pattern advocates that LSD1 is engaged at sauce chromatin zones linking with complexes of multi-protein having an exact DNA-binding transcription factor, establishing LSD1 as a favorable epigenetic target, and also gives a large selection of therapeutic targets to treat different tumors. This review sturdily backing the oncogenic probable of LSD1 in different tumors indicated that LSD1 levels can be used to monitor and identify different tumors and can be a useful biomarker of progression and fair diagnosis in tumor patients. The clinical trials showed that inhibitors of LSD1 have growing evidence of clinical efficacy which is very encouraging and promising. However, for some of the inhibitors such as GSK2879552, though selective, potent, and effective, its disease control was poor as the rate of adverse events (AEs) was high in tumor patients causing clinical trial termination, and continuation could not be supported by the risk-benefit profile. Therefore, we propose that, to attain excellent clinical results of inhibitors of LSD1, much attention is required in designing appropriate dosing regimens, developing in-depth in vitro/in vivo mechanistic works of LSD1 inhibitors, and developing inhibitors of LSD1 that are reversible, safe, potent, and selective which may offer safer profiles.

scholarly journals Virtual clinical trials identify effective combination therapies in ovarian cancer

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Emilia Kozłowska ◽  
Tuulia Vallius ◽  
Johanna Hynninen ◽  
Sakari Hietanen ◽  
Anniina Färkkilä ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Resistance Mechanisms ◽  
Added Value ◽  
Platinum Resistance ◽  
Mathematical Framework ◽  
Effective Combination

AbstractA major issue in oncology is the high failure rate of translating preclinical results in successful clinical trials. Using a virtual clinical trial simulations approach, we present a mathematical framework to estimate the added value of combinatorial treatments in ovarian cancer. This approach was applied to identify effective targeted therapies that can be combined with the platinum-taxane regimen and overcome platinum resistance in high-grade serous ovarian cancer. We modeled and evaluated the effectiveness of three drugs that target the main platinum resistance mechanisms, which have shown promising efficacy in vitro, in vivo, and early clinical trials. Our results show that drugs resensitizing chemoresistant cells are superior to those aimed at triggering apoptosis or increasing the bioavailability of platinum. Our results further show that the benefit of using biomarker stratification in clinical trials is dependent on the efficacy of the drug and tumor composition. The mathematical framework presented herein is suitable for systematically testing various drug combinations and clinical trial designs in solid cancers.

scholarly journals Generation of digital patients for the simulation of tuberculosis with UISS-TB

2020 ◽  
Vol 21 (S17) ◽  
Author(s):  
Miguel A. Juárez ◽  
Marzio Pennisi ◽  
Giulia Russo ◽  
Dimitrios Kiagias ◽  
Cristina Curreli ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Biological Diversity ◽  
Population Distribution ◽  
Target Population ◽  
Population Characteristics ◽  
Virtual Patients ◽  
Therapeutic Vaccines ◽  
Sequential Approach ◽  
Horizon 2020

Abstract Background The STriTuVaD project, funded by Horizon 2020, aims to test through a Phase IIb clinical trial one of the most advanced therapeutic vaccines against tuberculosis. As part of this initiative, we have developed a strategy for generating in silico patients consistent with target population characteristics, which can then be used in combination with in vivo data on an augmented clinical trial. Results One of the most challenging tasks for using virtual patients is developing a methodology to reproduce biological diversity of the target population, ie, providing an appropriate strategy for generating libraries of digital patients. This has been achieved through the creation of the initial immune system repertoire in a stochastic way, and through the identification of a vector of features that combines both biological and pathophysiological parameters that personalise the digital patient to reproduce the physiology and the pathophysiology of the subject. Conclusions We propose a sequential approach to sampling from the joint features population distribution in order to create a cohort of virtual patients with some specific characteristics, resembling the recruitment process for the target clinical trial, which then can be used for augmenting the information from the physical the trial to help reduce its size and duration.

scholarly journals Update on Functional Inhibitors of Acid Sphingomyelinase (FIASMAs) in SARS-CoV-2 Infection

2021 ◽  
Vol 14 (7) ◽  
pp. 691
Author(s):  
Gwenolé Loas ◽  
Pascal Le Corre
Keyword(s):  
Clinical Trials ◽  
In Silico ◽  
Acid Sphingomyelinase ◽  
In Vivo Studies ◽  
Controlled Clinical Trials ◽  
Off Label ◽  
Double Blinded

The SARS-CoV-2 outbreak is characterized by the need of the search for curative drugs for treatment. In this paper, we present an update of knowledge about the interest of the functional inhibitors of acid sphingomyelinase (FIASMAs) in SARS-CoV-2 infection. Forty-nine FIASMAs have been suggested in the treatment of SARS-CoV-2 infection using in silico, in vitro or in vivo studies. Further studies using large-sized, randomized and double-blinded controlled clinical trials are needed to evaluate FIASMAs in SARS-CoV-2 infection as off-label therapy.

Nature-derived hit, lead, and drug-like small molecules: Current status and future aspects against key target proteins of Coronaviruses

2021 ◽  
Vol 21 ◽  
Author(s):  
Md. Junaid ◽  
Yeasmin Akter ◽  
Aysha Siddika ◽  
S. M. Abdul Nayeem ◽  
Afsana Nahrin ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Small Molecules ◽  
Current Status ◽  
Future Research ◽  
Literature Study ◽  
Lead Compounds ◽  
Human Coronaviruses

Background: COVID-19 pandemic, the most unprecedented event of the year 2020, has brought millions of scientists worldwide in a single platform to fight against it. Though several drugs are now in the clinical trial, few vaccines available on the market already but the lack of an effect of those is making the situation worse. Aim of the study: In this review, we demonstrated comprehensive data of natural antiviral products showing activities against different proteins of Human Coronaviruses (HCoV) that are responsible for its pathogenesis. Furthermore, we categorized the compounds into the hit, lead, and drug based on the IC50/EC50 value, drug-likeness, and lead-likeness test to portray their potentiality to be a drug. We also demonstrated the present status of our screened antiviral compounds with respect to clinical trials and reported the lead compounds that can be promoted to clinical trial against COVID-19. Methods: A systematic search strategy was employed focusing on Natural Products (NPs) with proven activity (in vitro, in vivo, or in silico) against human coronaviruses, in general, and data were gathered from databases like PubMed, Web of Science, Google Scholar, SciVerse, and Scopus. Information regarding clinical trials retrieved from the Clinical Trial database. Results: Total "245" natural compounds were identified initially from the literature study. Among them, Glycyrrhizin, Caffeic acid, Curcumin is in phase 3, and Tetrandrine, Cyclosporine, Tacrolimus, Everolimus are in phase 4 clinical trial. Except for Glycyrrhizin, all compounds showed activity against COVID-19. Conclusions: In summary, our demonstrated specific small molecules with lead and drug-like capabilities clarified their position in the drug discovery pipeline and proposed their future research against COVID-19.

scholarly journals In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome

2021 ◽  
Author(s):  
Jeroen H.A. Creemers ◽  
Kit C.B. Roes ◽  
Niven Mehra ◽  
Carl G. Figdor ◽  
I. Jolanda M. de Vries ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Tumor Microenvironment ◽  
Cancer Immunotherapy ◽  
In Silico ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Specific Response ◽  
Response Patterns ◽  
Biological Interactions

ABSTRACTBackgroundLate-stage cancer immunotherapy trials strive to demonstrate the clinical efficacy of novel immunotherapies, which is leading to exceptional responses and long-term survival in subsets of patients. To establish the clinical efficacy of an immunotherapy, it is critical to adjust the trial’s design to the expected immunotherapy-specific response patterns.MethodsIn silico cancer immunotherapy trials are virtual clinical trials that simulate the kinetics and outcome of immunotherapy depending on the type and treatment schedule. We used an ordinary differential equation model to simulate (1) cellular interactions within the tumor microenvironment, (2) translates these into disease courses in patients, and (3) assemble populations of virtual patients to simulate in silico late-stage immunotherapy, chemotherapy, or combination trials. We predict trial outcomes and investigate how therapy-specific response patterns affect the probability of their success.ResultsIn silico cancer immunotherapy trials reveal that immunotherapy-derived survival kinetics – such as delayed curve separation and plateauing curve of the treatment arm – arise naturally due to biological interactions in the tumor microenvironment. In silico clinical trials are capable of translating these biological interactions into survival kinetics. Considering four aspects of clinical trial design – sample size calculations, endpoint and randomization rate selection, and interim analysis planning – we illustrate that failing to consider such distinctive response patterns can significantly reduce the power of novel immunotherapy trials.ConclusionIn silico trials have three significant implications for immuno-oncology. First, they provide an economical approach to verify the robustness of biological assumptions underlying an immunotherapy trial and help to scrutinize its design. Second, the biological basis of these trials facilitates and encourages communication between biomedical researchers, doctors, and trialists. Third, its application as an educational tool can illustrate design principles to scientists in training, contributing to improved designs and higher success rates of future immunotherapy trials.

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