Clinical & Scientific Research
To gather data on potential application to new diseases and disorders is increasingly to be not only a means for evaluating the effectiveness of new medicine and pharmaceutical formulas but also for experimenting on existing drugs and their appliance to new diseases and disorders.
The latter is due to the crisis that is being noticed in the pharmaceutical research. According to empirical studies, the number of medicines introduced worldwide containing new active ingredients dropped from an average of over 60 a year in the late 1980s to 52 in 1991, only 31 in 2001 (Van den Haak et al, 2002) and around 20–25 new licensed drugs per year over the past years (Fisk and Atun, 2008). Aspirin and beta blockers comprise two most well-known examples; initially, aspirin was known for its analgesic, anti-inflammatory and antipyretic properties. However, aspirin's effects on blood clotting (as an antiplatelet agent) were first noticed in 1950 and since the end of the 1980s, low-dose aspirin has been widely used as a preventive drug for heart attacks. Interestingly, beta blockers, which were thought to be detrimental for heart failure, appeared to be beneficial and have changed the adverse course of heart failure. In the meanwhile, the overall number of new active substances undergoing regulatory review is gradually falling, whereas pharmaceutical companies tend to prefer launching modified versions of existing drugs, which present reduced risk of failure and can generate generous profits. This approach extends to the ongoing attempts by pharmaceutical companies to extend the period of time under patent protection for a given drug and its associated family of products. This phenomenon has been even more intensified by the world economy shrinking which causes reduction in the allocation of funds for new research vs re-positioning of existing medications for new uses.
In the meanwhile, treatments with high efficacy may be limited by severe side effects or efficacy may be lost in translation. Translation into clinical therapy has to overcome substantial barriers at the preclinical and clinical levels. Thus, bridging basic science to clinical practice comprises a new scientific challenge which can result in successful clinical applications with low financial cost. In the aforementioned context, the results yielding from clinical trials, which are testing the effectiveness of existing drugs and pharmaceutical formulas on diseases other than the ones they are currently treating, are closely dependent on the available data and the patients. Such trials require the pursuit of a number of aspects that need to be addressed ranging from the aggregation of data from various heterogeneous distributed sources (such as electronic health records - EHRs) to the intelligent processing of this data based on the clinical trial-specific requirements for choosing the appropriate patients eligible for recruitment.
Nevertheless, clinical trials quite often fail to demonstrate any beneficial effect and sometimes overestimate the unwanted effects with their results having low external validity. In fact, when some evidence-based treatments are applied in practice, the outcomes are much less favourable than would be expected from the results of clinical trials (Taylor et al, 2007). Clinical trials usually focus on single interventions, whereas the clinical practice environment comprises of various features such as intercurrent illnesses, use of other drugs, mood, compliance, that need to be taken into account (Wilcken et al, 2007) – a fact that is driven mainly by the non-representative sample of patients recruited for participation in clinical trials. This great deviation between the two areas limits the validity of the results from the clinical trials and the medical community's understanding of how widely these results can in fact be applied while ensuring the patients' safety. Evans and Clara (2001) indicate in their research that trials aiming to prevent stroke using antithrombotic therapies among patients with atrial fibrillation have recruited as few as 20% of eligible patients, often excluding older patients, women and people with previous cerebrovascular disease, leading to uncertainty about the net benefit of such treatment in these groups. Moreover, clinical trials' results on a drug may show that mortality rates are lower than 3% whereas in real life this rate may prove to be greater than 25%, placing the patients' safety in great danger. Poor trial design, lack of proper funding, lack of access to and linking with important data (especially Electronic Health Record of patients), a non-representative resulting patient sample recruited for the clinical trial and the inability to predict off-target effects and potential at-risk populations comprise main factors driving to these major problems seriously affecting patients' safety. In fact and as shown above, particular attention should be directed towards patient selection, ensuring that the study groups are well balanced.
In the meanwhile, the need for optimization of the patient recruiting operations is further intensified by pharmaceutical companies and clinical research organizations needs. Currently tremendous market opportunities for potential blockbusters may be delayed due to operational difficulties in clinical trial design and implementation. With limited patent lifetime protection and increased risk from generic competition, the onus on optimizing the most costly phase of drug development, clinical trials, looms as the key for enhanced return on investment in the industry and improving the long-term access to improved medicines for the patients and physicians. Many drugs designed for attacking very specific biological targets pose significant limitations in the medical profile of the patients eligible to participate in their clinical trials; lack of access to a large patient pool through proper linking of complex systems with disparate clinical care systems leads to operational delays and quite often to inadequate inclusion of critical study populations. In fact, almost half of the delays in clinical trials are due to patient recruitment problems. This way patent exclusivity time is reduced and the most commercially productive phase of a drug's life cycle is significantly shortened with the pharmaceutical companies and the clinical research organizations facing many difficulties in gaining a competitive edge (Business Insights, 2007).
Data collection poses a significant challenge for investigators, due to the non-interoperable heterogeneous distributed data sources involved. A great amount of medical information crucial to the success of a clinical trial could be hidden inside a variety of information systems that do not share the same semantics or adhere to widely deployed clinical data standards. The interoperability of Electronic Health Record systems with clinical research information systems can significantly contribute to the successful conduction of any clinical trial the results of which are of great validity, since without access to high-quality patient data any attempt for accurate, fast and reliable patient selection, as well as useful and effective analysis of the trial results, will certainly fail.
Towards this direction, ICT may play a key role with the provision of tools and infrastructures that will enable the doctors and researchers to compose well-designed clinical trials and to automatically identify the patients eligible to participate into such trials by taking into consideration a number of factors related to the patients' safety, the expected efficacy of the clinical trial and the related cost. Thus, the recruitment process will not only be improved in terms of optimized patients' selection but also in terms of time and cost-effectiveness with the use of ICT tools and infrastructures.
Based on the above, PONTE aims at providing a platform following a Service Oriented Architecture (SOA) approach that will effectively guide medical researchers through clinical trial design and offer intelligent automatic identification of individuals eligible (concerning their safety and clinical trial goals) to participate in the clinical trials. Work towards this direction will involve the development of advanced decision support mechanisms based on risk assessment and techno-economical models which will be fed with information intelligently retrieved from an innovative semantic search engine operating on top of a novel data representation with excessive descriptive power linking data within drug and disease knowledge databases, clinical care and clinical research information systems.