scholarly journals Artificial intelligence in clinical research

2016 ◽  
Vol 3 (4) ◽  
pp. 187 ◽  
Author(s):  
Veerabhadra Sanekal Nayak ◽  
Mohammed Saleem Khan ◽  
Bharat Kumar Shukla ◽  
Pranjal R. Chaturvedi

<p>Envision dedicating fifteen years to a critical interest and emptying staggering amount of funds into it, at the same time confronting a disappointment rate of 95 percent. That is the crippling reality for pharmaceutical organizations, which toss billions of dollars consistently toward medications that possible won't work – and after that do a reversal to the planning phase and do it once more. Today's medications go to the business sector after an extensive, very costly process of drug development. It takes anywhere in the range of 10 to 15 years, here and there significantly more, to convey a medication from introductory revelation to the hands of patients – and that voyage can cost billions up to 12 billion, to be correct. That is just a lot to spend, and excessively yearn for patients to hold up. Patients can hardly wait 15 years for a lifesaving drug, we require another productive focused on medication revelation and improvement process. Artificial Intelligence, can significantly reduce the time included, and also cut the expenses by more than half. This is made conceivable through a totally distinctive way to deal with medication revelation. With the present technique, for each 100 medications that achieve first stage clinical trials, only one goes ahead to wind up a genuine treatment. That is stand out percent, it's an unsustainable model, particularly when there are ailments, for example, pancreatic malignancy which has a normal five-year survival rate of 6%.</p>

Author(s):  
Michael Tansey

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.


Author(s):  
Masturah Bte Mohd Abdul Rashid

The inverse relationship between the cost of drug development and the successful integration of drugs into the market has resulted in the need for innovative solutions to overcome this burgeoning problem. This problem could be attributed to several factors, including the premature termination of clinical trials, regulatory factors, or decisions made in the earlier drug development processes. The introduction of artificial intelligence (AI) to accelerate and assist drug development has resulted in cheaper and more efficient processes, ultimately improving the success rates of clinical trials. This review aims to showcase and compare the different applications of AI technology that aid automation and improve success in drug development, particularly in novel drug target identification and design, drug repositioning, biomarker identification, and effective patient stratification, through exploration of different disease landscapes. In addition, it will also highlight how these technologies are translated into the clinic. This paradigm shift will lead to even greater advancements in the integration of AI in automating processes within drug development and discovery, enabling the probability and reality of attaining future precision and personalized medicine.


2010 ◽  
Vol 9 (4) ◽  
pp. 214-219
Author(s):  
Robyn J. Barst

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?


2019 ◽  
Author(s):  
Allison Hirsch ◽  
Mahip Grewal ◽  
Anthony James Martorell ◽  
Brian Michael Iacoviello

BACKGROUND Digital Therapeutics (DTx) provide evidence based therapeutic health interventions that have been clinically validated to deliver therapeutic outcomes, such that the software is the treatment. Digital methodologies are increasingly adopted to conduct clinical trials due to advantages they provide including increases in efficiency and decreases in trial costs. Digital therapeutics are digital by design and can leverage the potential of digital and remote clinical trial methods. OBJECTIVE The principal purpose of this scoping review is to review the literature to determine whether digital technologies are being used in DTx clinical research, which type are being used and whether publications are noting any advantages to their use. As DTx development is an emerging field there are likely gaps in the knowledge base regarding DTx and clinical trials, and the purpose of this review is to illuminate those gaps. A secondary purpose is to consider questions which emerged during the review process including whether fully remote digital clinical research is appropriate for all health conditions and whether digital clinical trial methods are inline with the principles of Good Clinical Practice. METHODS 1,326 records were identified by searching research databases and 1,227 reviewed at the full-article level in order to determine if they were appropriate for inclusion. Confirmation of clinical trial status, use of digital clinical research methods and digital therapeutic status as well as inclusion and exclusion criteria were applied in order to determine relevant articles. Digital methods employed in DTx research were extracted from each article and these data were synthesized in order to determine which digital methods are currently used in clinical trial research. RESULTS After applying our criteria for scoping review inclusion, 11 articles were identified. All articles used at least one form of digital clinical research methodology enabling an element of remote research. The most commonly used digital methods are those related to recruitment, enrollment and the assessment of outcomes. A small number of articles reported using other methods such as online compensation (n = 3), or digital reminders for participants (n = 5). The majority of digital therapeutics clinical research using digital methods is conducted in the United States and increasing number of articles using digital methods are published each year. CONCLUSIONS Digital methods are used in clinical trial research evaluating DTx, though not frequently as evidenced by the low proportion of articles included in this review. Fully remote clinical trial research is not yet the standard, more frequently authors are using partially remote methods. Additionally, there is tremendous variability in the level of detail describing digital methods within the literature. As digital technologies continue to advance and the clinical research DTx literature matures, digital methods which facilitate remote research may be used more frequently.


Author(s):  
Elizabeth Biswell R ◽  
Michael Clark ◽  
Michela Tinelli ◽  
Gillian Manthorpe ◽  
Joanne Neale ◽  
...  

Author(s):  
C. Madeira ◽  
L. Hořavová ◽  
F. dos Santos ◽  
J. R. Batuca ◽  
K. Nebeska ◽  
...  

Abstract Objectives Clinical trials provide one of the highest levels of evidence to support medical practice. Investigator initiated clinical trials (IICTs) answer relevant questions in clinical practice that may not be addressed by industry. For the first time, two European Countries are compared in terms of IICTs, respective funders and publications, envisaging to inspire others to use similar indicators to assess clinical research outcomes. Methods A retrospective systematic search of registered IICTs from 2004 to 2017, using four clinical trials registries was carried out in two European countries with similar population, GDP, HDI and medical schools but with different governmental models to fund clinical research. Each IICT was screened for sponsors, funders, type of intervention and associated publications, once completed. Results IICTs involving the Czech Republic and Portugal were n = 439 (42% with hospitals as sponsors) and n = 328 (47% with universities as sponsors), respectively. The Czech Republic and Portuguese funding agencies supported respectively 61 and 27 IICTs. Among these, trials with medicinal products represent 52% in Czech Republic and 4% in Portugal. In the first, a higher percentage of IICTs’ publications in high impact factor journals with national investigators as authors was observed, when compared to Portugal (75% vs 15%). Conclusion The better performance in clinical research by Czech Republic might be related to the existence of specific and periodic funding for clinical research, although further data are still needed to confirm this relationship. In upcoming years, the indicators used herein might be useful to tracking clinical research outcomes in these and other European countries.


Author(s):  
Demissie Alemayehu ◽  
Robert Hemmings ◽  
Kannan Natarajan ◽  
Satrajit Roychoudhury

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ciska Verbaanderd ◽  
Ilse Rooman ◽  
Isabelle Huys

Abstract Background Finding new therapeutic uses for existing medicines could lead to safe, affordable and timely new treatment options for patients with high medical needs. However, due to a lack of economic incentives, pharmaceutical developers are rarely interested to invest in research with approved medicines, especially when they are out of basic patent or regulatory protection. Consequently, potential new uses for these medicines are mainly studied in independent clinical trials initiated and led by researchers from academia, research institutes, or collaborative groups. Yet, additional financial support is needed to conduct expensive phase III clinical trials to confirm the results from exploratory research. Methods In this study, scientific and grey literature was searched to identify and evaluate new mechanisms for funding clinical trials with repurposed medicines. Semi-structured interviews were conducted with 16 European stakeholders with expertise in clinical research, funding mechanisms and/or drug repurposing between November 2018 and February 2019 to consider the future perspectives of applying new funding mechanisms. Results Traditional grant funding awarded by government and philanthropic organisations or companies is well known and widely implemented in all research fields. In contrast, only little research has focused on the application potential of newer mechanisms to fund independent clinical research, such as social impact bonds, crowdfunding or public-private partnerships. Interviewees stated that there is a substantial need for additional financial support in health research, especially in areas where there is limited commercial interest. However, the implementation of new funding mechanisms is facing several practical and financial challenges, such as a lack of expertise and guidelines, high transaction costs and difficulties to measure health outcomes. Furthermore, interviewees highlighted the need for increased collaboration and centralisation at a European and international level to make clinical research more efficient and reduce the need for additional funding. Conclusions New funding mechanisms to support clinical research may become more important in the future but the unresolved issues identified in the current study warrant further exploration.


2021 ◽  
Vol 14 (3) ◽  
pp. 280
Author(s):  
Rita Rebelo ◽  
Bárbara Polónia ◽  
Lúcio Lara Santos ◽  
M. Helena Vasconcelos ◽  
Cristina P. R. Xavier

Pancreatic ductal adenocarcinoma (PDAC) is considered one of the deadliest tumors worldwide. The diagnosis is often possible only in the latter stages of the disease, with patients already presenting an advanced or metastatic tumor. It is also one of the cancers with poorest prognosis, presenting a five-year survival rate of around 5%. Treatment of PDAC is still a major challenge, with cytotoxic chemotherapy remaining the basis of systemic therapy. However, no major advances have been made recently, and therapeutic options are limited and highly toxic. Thus, novel therapeutic options are urgently needed. Drug repurposing is a strategy for the development of novel treatments using approved or investigational drugs outside the scope of the original clinical indication. Since repurposed drugs have already completed several stages of the drug development process, a broad range of data is already available. Thus, when compared with de novo drug development, drug repurposing is time-efficient, inexpensive and has less risk of failure in future clinical trials. Several repurposing candidates have been investigated in the past years for the treatment of PDAC, as single agents or in combination with conventional chemotherapy. This review gives an overview of the main drugs that have been investigated as repurposing candidates, for the potential treatment of PDAC, in preclinical studies and clinical trials.


Sign in / Sign up

Export Citation Format

Share Document