Digital Therapeutics

Digital Therapeutics

State of the Art 

Living and ageing in good health, which has always been a human ambition, has now become a priority for both the individual and the whole society, to be pursued with targeted, coordinated and continuous actions, also aimed to ensuring the sustainability of health systems.

Although health interventions are only one of the determinants of health, preventing and treating diseases is a fundamental aspect of contributing to healthy ageing and the search for new and more effective interventions is a necessary condition for maintaining their effectiveness over time and responding to the emerging health needs of an ageing population.

The need to increase the effectiveness and breadth of health interventions while containing their costs is both a challenge and a major driver for the digital transformation of medicine and health and the development of Digital Health.

Digital Healthi s a relatively recent concept, still often confused, which covers all aspects related to the use of digital technologies to improve the human health. In most cases, the Digital Health Technologies(DHT) available today are concerned with wellbeing (monitoring of food calories, promotion of physical activity, promotion of good sleep, clinical diaries and others).

Digital Health encompasses both aspects of health managed by health care (telemedicine, information systems and electronic health records) and aspects related to the management of individual health through applications on mobile devices (mApps), sensors and other technologies for personal use, able to generate data and information that are not always integrated with health information systems today. In both cases, the ultimate goal is to make health "smarter" (Smart Health), i.e. more efficient health services and absolute gain in health.

Within the concept of Digital Health, an area more recently defined is represented by Digital Medicine(1), i.e. DHT with the specific purpose of (a) measuring biological or functional parameters (b) intervene with the aim of improving the health status.

In the first case there are several DHT based on sensors, such as bracelets, rings, applications for smartphones capable of recording parameters and possibly predicting the occurrence of future events, such as hypoglycemic crises or seizures (2).

The second includes interventions for therapeutic purposes of various kinds, such as telemedicine, sensors for monitoring therapeutic adherence (3), Digital Therapeutics.

 

Digital Health per Smart Health 

Giuseppe Recchia, Fondazione Smith Kline, Verona; daVinci Digital Therapeutics 

Among the areas of major interest of Digital MedicineDigital Therapycan be described as 

·     a health care intervention for therapeutic purposes, therefore intended for a person with a diagnosis of disease and delivered with the intent to improve his/her health, 

·     in which the software is the active ingredient responsible for the clinical benefit, 

·     developed through controlled clinical trials (RCTs),

·     authorized by regulatory bodies for its use, 

·     subject to technological evaluation (HTA) for the definition of its value, 

·     reimbursed by public health systems and/or private insurances, 

·     prescribed by a physician (4)

 

What qualifies it as a therapy and therefore different  from other digital health technologies is therefore the documentation of effectiveness and tolerability generated through RCTs for the generation of clinical data.

The first Digital Therapeutic (reSET for substance use disorder) was approved by the FDA in 2017, while other DTx are already used in Europe and in some countries also reimbursed.

As this is an emerging digital health technology, a number of aspects have not yet been defined and clarified:

- clinical development,

- regulatory assessment,

- reimbursement coverage,

- integration into healthcare.

Digital Therapy is not just a tool for health. Software and digital processes represent new areas of economic development. Creating enabling conditions for the research, development and introduction of Digital Therapies in health care processes is a necessary condition to compete globally in this area of medicine.

In order to realize the promises of Digital Health Technologies, there are several barriers to overcome, the first of which is the lack of confidence of stakeholders in the ability to keep these promises, due to the limited technical and clinical validation of these promises (6).

Digital Health, Digital Medicineand Digital Therapy are today emerging areas set to become the reference for health management in the 20’. The way in which the discussion on their development and implementation is addressed today will determine the model with which these new technologies will be evaluated and then implemented. They could be considered as a threat to be limited and blocked (as happened to Huber in the transport sector in many European countries) or on the other side, an opportunity for which to create enabling conditions for both health and economic development. 

The involvement of stakeholders, first and foremost citizens and patients, the quality of research and development on DTH and the social discussion in the coming years will be crucial in defining and guiding the way forward.

 

Selected references 

1.    Coravos A et al. Digital Medicine : a Primer on Measurement. Digit Biomark 2019;3:31–71

2.   Embrace by Empatica Receives First of Its Kind FDA Clearance in Epilepsy for Children https://www.prnewswire.com/news-releases/embrace-by-empatica-receives-first-of-its-kind-fda-clearance-in-epilepsy-for-children-819639106.html

3.   The FDA has approved the first digital pill https://www.theverge.com/2017/11/14/16648166/fda-digital-pill-abilify-otsuka-proteus

4.  Digital Therapeutics Alliance . Digital Therapeutics: Combining Technology and Evidence-based Medicine to Transform Personalized Patient Care. https://www.dtxalliance.org/wp-content/uploads/2018/09/DTA-Report_DTx-Industry-Foundations.pdf

5.   Pear Therapeutics Presents New Data on reSET and reSET-O at American Academy of Addiction Psychiatry Annual Meeting and Scientific Symposium. https://peartherapeutics.com/pear-therapeutics-presents-new-data-on-reset-and-reset-o-at-american-academy-of-addiction-psychiatry-annual-meeting-and-scientific-symposium/

6.  Mathews S et al. Digital health: a path to validation. Digital Medicine (2019) 2:38

 

Artificial Intelligence in Clinical Research 

Massimo Beccaria, Alfa Technologies International, polihub Milano

The world is evolving in every sector thanks to the great driver of technologies that invade our lives, every day more and more.

The world of research, as already happened with those of logistics, mechanics, and telecommunications, is beginning a metamorphosis that will change its current connotations and project it into a future of challenges and new achievements, thanks to the opportunities introduced by digital technologies, such as artificial intelligence.

The planned intervention is intended to be a starting point for reflection on how this scenario, which is only now beginning to be glimpsed, is shaping the current world and how it will irrevocably determine that of the immediate future, what obstacles the Italian system, on the other hand, enmeshes with respect to international dynamics, which is capable of generating talent in this field but is often unable to exploit them.

The A.I. applied to clinical trials is already able to shorten times and optimize processes more and more, but like any technology must be managed and standardized, consistent with the objectives set.

Today, therefore, there are difficult challenges that see us once again involved in having to find the right compromises between systems in rapid evolution, exciting expectations and organizational and cultural limits.

 

Selected references 

  1. Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry https://www.biopharmatrend.com/post/72-2018-ai-is-surging-in-drug-discovery-market

  2. Towards a More Competitive Italy in Clinical Research: The Survey of Attitudes towards Trial sites in Europe (The SAT-EU Study TM) - Marta Gehring, Claudio Jommi, Rosanna Tarricone, Mariapia Cirenei, Giuseppe Ambrosio https://ebph.it/article/view/10246

3.   7 Reasons Why Clinical Trials Fail https://cyntegrity.com/7-reasons-clinical-trials-fail/

4.  The age of Big Data and the power of Watson https://www.ema.europa.eu/en/documents/presentation/presentation-age-big-data-power-watson-lisa-latts_en.pdf

5.   Startups using artificial intelligence in drug discovery https://blog.benchsci.com/startups-using-artificial-intelligence-in-drug-discovery

 

In Silico Clinical Trials 

Enrico Tronci, Computer Science Department, Sapienza University of Rome 

In silico clinical trials (ISCT) aim at replacing the first stages of in vivo clinical trials with computer simulations.

This enables safety and efficacy assessment of drugs when there are not enough patients for in vivo clinical trials (e.g., rare diseases) and, more in general, holds the promise of decreasing time and cost of safety and efficacy assessment for drugs and biomedical devices, even when patients may be available.

Through computational models (virtual patients) ISCT predicts, through simulation, the effect of a drug or biomedical device on (real) patients. Basically, ISCT rests on Physiologically based Pharmacokinetic/Pharmacodynamic (PBPK/PD) models as well as on software tools supporting their construction and automatic analysis. In this talk, through examples, we will outline some ISCT methods, software tools and open problems.

 

Selected references 

  1. Minimizing Resistance Consequences After Virologic Failure on Initial Combination Therapy: A Systematic Overview - Bartlett, John A., et al. CROI, 2006

2.  Dolutegravir (DTG; S/GSK1349572) in Combination Therapy Exhibits Rapid and Sustained Antiviral Response in Antiretroviral‐ Naïve Adults: 96‐Week Results from SPRING‐1 (ING112276) - Stellbrink, H.J., et al. CROI, 2012

3.  Sax P., et al. CROI, 2012 

4.  DeJesus, E., et al. CROI 2012

5.  FDA analysis of enrollment of older adults in clinical trials for cancer drug registration: A 10-year experience by the U.S. Food and Drug Administration 

6.  FDA: Drug Approval Process, 2015 https://www.fda.gov/drugs/new-drugs-fda-cders-new-molecular-entities-and-new-therapeutic-biological-products/novel-drug-approvals-2015

7.  Trends in risks associated with new drug development: success rates for investigational drugs. Clin Pharmacol Ther. 2010 https://www.ncbi.nlm.nih.gov/pubmed/20130567

8.  FDA: Drug review Process 2015 https://www.fda.gov/drugs/drug-information-consumers/fdas-drug-review-process-continued

9.  Discovery Medicine: The Cost of New Drug Discovery and Development http://www.discoverymedicine.com/Michael-Dickson/2009/06/20/the-cost-of-new-drug-discovery-and-development/

10.  Challenges and Opportunities: Workshop Summary. Institute of Medicine (US) Forum on Drug Discovery, Development, and Translation. Washington (DC): National Academies Press (US); 2010. https://www.ncbi.nlm.nih.gov/books/NBK50892/

11.  https://medicalfuturist.com/future-of-clinical-trials

12.  Morrison T. M., et al. Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167449/

13.  https://www.fda.gov/about-fda/cdrh-offices/virtual-family

14.  https://www.fda.gov/regulatory-information/search-fda-guidance-documents/reporting-computational-modeling-studies-medical-device-submissions

15.  Haddad, et al., Reliability Engineering & System Safety, 123 (2014) https://www.journals.elsevier.com/reliability-engineering-and-system-safety

16.  https://figshare.com/articles/FDA_Seminar_on_Computational_Modeling_for_Medical_Devices/5018783/1 ù

17.   Kuepfer L.. et al., Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5080648/

18.   Jones, H. M., & Rowland-Yeo, K. (2013). Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. https://www.ncbi.nlm.nih.gov/pubmed/23945604

19.  Schaller S., et al., A Generic Integrated Physiologically based Whole-body Model of the Glucose-Insulin-Glucagon Regulatory System https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3828004/

20.  Schaller S., et al., Robust PBPK/PD-Based Model Predictive Control of Blood Glucose. https://www.ncbi.nlm.nih.gov/pubmed/26552072

21.  Röblitz S., et al., A mathematical model of the human menstrual cycle for the administration of GnRH analogues. https://www.ncbi.nlm.nih.gov/pubmed/23206386

 

 

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