Database design and implementation overview

Database Presentation

The objective of this study is to present a new database to be implemented in the clinical setting to increase quality and safety of patient care.

The database at focus in this work in writing is that of the Targeted Therapy Database (TTD) in a Cancer unit in a hospital. The work of Mocellin, et al. (2010) relates that the "efficacy of current anticancer treatments is far from satisfactory and many patients still die of their disease. A general agreement exists on the urgency of developing molecularly targeted therapies, although their implementation in the clinical setting is in its infancy. In fact, despite the wealth of preclinical studies addressing these issues, the difficulty of testing each targeted therapy hypothesis in the clinical arena represents an intrinsic obstacle. As a consequence, we are witnessing a paradoxical situation where most hypotheses about the molecular and cellular biology of cancer remain clinically untested and therefore do not translate into a therapeutic benefit for patients." (p.1) According to Mocellin (2010) Cancer is representative of the "third leading cause of death throughout the world and second in Western countries. However, early diagnosis is the best possible way of cure for the majority of types of cancer. Targeted therapy is reported to include "those approaches that aim to tailor the therapy to the patient (or cohort of patients) based on specific molecular features of the disease- and/or patient. The ultimate goal is obviously to maximize the therapeutic efficacy while minimizing the toxicity, that is, increasing the therapeutic index. In cancer medicine, tumor-specific molecular derangements (e.g., gene mutation or protein overactivation), are the ideal targets for therapeutic strategies aimed to kill malignant cells while sparing normal cells. Furthermore, patient-specific molecular features such as polymorphisms of detoxifying enzymes can affect the metabolism of anticancer drugs and thus can play a role in both efficacy and toxicity profiles. According to these principles, personalized targeted therapy includes not only the development and clinical implementation of "smart" drugs (i.e., agents that target tumor-specific molecular derangements), but also the identification of the patient molecular profile that maximizes the therapeutic index of "conventional" chemotherapeutics." (Mocellin, et al., 2010, p.1)

Two mainstreams of research in the field of targeted anticancer therapy are those stated as follows:

(1) to develop novel therapeutic agents based on the molecular "Achilles' heel(s)" of malignant cells, which usually implies the selection of patients bearing a cancer that harbors that specific molecular derangement (Mocellin, et al., 2010, p.1)

(2) to identify biomarker(s) predictive of tumor responsiveness based on the molecular characteristics of both the patient and the tumor; this approach, ultimately, would lead to administer conventional and/or targeted drugs only to patients with the greatest likelihood of responding and the least likelihood of suffering from side effects. (Mocellin, et al., 2010, p.1)

According to Mocellin et al. The hurdles to effectively using targeted therapy in treating cancer include:

(1)elucidation of the molecular pathways governing disease development and progression has provided investigators with numerous potential new therapeutic targets, but has at the same time exponentially increased the number of variables that must be taken into account when designing new drugs and trials (Mocellin, et al., 2010, p.1)

(2) the ever growing amount of information generated by the scientific community stands in striking contrast to the parallel lack of publicly available bioinformatic tools capable of integrating data and knowledge in a rationally organized, biologically informative and therapeutically oriented manner, which would maximize the likelihood of finding the shortest path to effective cancer treatments (Mocellin, et al., 2010, p.1)

(3) therapy personalization requires the study of molecular profiles on a single-patient basis, which requires the availability of huge computable biological databanks; a formidable corollary issue is that data sharing implies the compatibility of different technological platforms used around the world by different investigators (Mocellin, et al., 2010, p.1);

(4) the costs for the development and the production of "smart" drugs may pose problems of expenses that cannot be sustained by either public or private research institutions or even by national health care system. (Mocellin, et al., 2010, p.1)

I. Targeted Therapy Database

The Targeted Therapy Database (TTD) is reported to be "a systematic collection of the scientific knowledge regarding the development of targeted therapy for melanoma. A copy of the database is available as an open-access file in the MMMP website (http://www.mmmp.org)." (Mocellin, et al., 2010, p.1) The intentions of the database is to "gather in a standardized and computationally oriented fashion the published evidence on the molecular features that have been so far investigated to develop melanoma-specific therapies." (Mocellin, et al., 2010, p.1) The user can query the database for the following:

(1) To provide both basic researchers and clinical investigators with an unprecedented synopsis of the available scientific literature regarding the targeted therapy of melanoma (Mocellin, et al., 2010, p.1)

(2) To obtain summaries of the current evidence about the relationship between single molecules (or set of molecules) and the efficacy (or toxicity) of a given therapeutic agent (or set of therapeutic agents); summaries regarding the synergisms between drugs (conventional and/or targeted drugs) can also be obtained (Mocellin, et al., 2010, p.1); and (3) To match the patient (cancer) molecular profile with the available scientific evidence about the targeted therapy of melanoma, thus developing a drug ranking system for the personalized treatment of melanoma. (Mocellin, et al., 2010, p.1)

Mocellin et al. states that the information collected in the TTD makes provision of "…an overall picture of the data produced by the scientific community with regard to anti-melanoma targeted therapy, which are currently scattered in thousands of individual articles published in hundreds of journals often not open-access. Even more importantly, the computational analysis of the TTD data may prove useful to promote both the preclinical and clinical development of patient-tailored therapy based on the comprehensive (instead of piecewise) use of the available evidence." (Mocellin, et al., 2010, p.1)

The sources of information input in the TTD include: (1) PubMed; (2) Medline; (3) Embase; (4) Cancerlit; and (5) Cochrane databases. (Mocellin, et al., 2010, p.1) The database contains 15 columns including the identifying number, the source, the molecule, the molecule's alias, the conditions of the molecule, the modifier, the modifier alias, the relationship between the molecule and the corresponding treatment; the drug, the drug alias, the model (there are seven of these), the hypothesis, the cases, the reference and notes.

The TTD has as its goal enabling investigators to locate targeted therapy related information that is organized "in a standardized and computationally oriented fashion. For example the following illustration shows the example of evidence synopsis regarding the targeted therapy of melanoma as obtained by searching the Targeted Therapy Database.

Figure 1

Source: Mocellin, et al. (2010)