Companion Diagnostics Translational Medicines

Translational medicine is a new discipline, which covers studies on basic science, on human investigations, non-human investigations, and translational research (Mankoff et al. 2004). Basic science studies address the biological effects of medicines on human beings. Studies on humans discover the biology of disease and serve as foundation for developing therapies. Non-human or non-clinical studies advance therapies for clinical use or use in human disease. And translational research refers to appropriate product development for clinical use. Translational research looks into the identity, purity and potency of a drug product during early clinical trial (Mankoff et al.). Translating the knowledge derived from basic sciences into clinical research and treatments is the task of translational medicine (Nagappa 2006). There is a groaning need for this type of research on account of voluminous information in the information age. Using this information is the challenge encountered by scientists and healthcare providers everywhere in the world today. Clinical research disclosed this limitation in current clinical practice. The situation led to the development of the new discipline of translational medicine (Nagappa). Translational research connects basic research to its application in the clinical setting (Mariani, 2003). The clinical setting consists of the diagnosis and treatment or prevention of disease. It applies a discovery to practice. Edward Jenner is credited for the first clinical experiments in developing a smallpox vaccine in 1830 (Mariani).

Translational Research

A recent survey conducted by the Science Affairs Committee revealed that roughly 1,888 MD PhDs; 3,194 MDs; 5,703 PhDs; and 480 other qualified professionals or a total of more than 11,000 researchers are engaged in basic and clinical oncology research alone in the United States today (Mariani 2003). Developing new therapeutic strategies is lengthy. The research and development phase takes, on the average, 1-3 years; clinical trials phase, 2-10 years; and filings and approvals, 2 months to 3 years. On the whole, it takes 3-16 years to introduce new treatment into clinical practice in the United States (Mariani).

Teamwork is most important in every phase (Mariani 2003). This spans discovery, transfer to the clinic, evaluation of patient samples, rethinking of treatment and delivery. The team may be much more effective under the leadership of a scientifically trained and expert physician. This setup enables the right questions to raise the right questions while designing experimental standards. It will avoid dead-ends. Other factors, which enhance successful translational research, are the availability of mentors, time, rewards and sufficient resources. Factors, which deter or inhibit research success, include distractions from other official duties, the right funding, differences in knowledge among the researchers, technology transfers and strict schedule. Ultimate success, however, rests in high professional standards among those involved in the task. This is a problem at present in that there is a shortage of trained medical personnel in the United States and countries conducting such research. Physician scientists are particularly short of supply in the fields of radiology, pathology and surgery. They are those who can effectively communicate with clinicians. As it is, there is a wide gap between physicians who know the problems and the scientists who know the solutions. The chronic and global shortage of nurses aggravates the problem. The situation may be hinting at a lack of appropriate education and rewards to brilliant, high-performing professionals who observe high ethical standards. It may also be hinting at a lack of adequate resources and processes that will insure accurate and reliable assessments of new treatment strategies evolved (Mariani).

Translational Medicine and Drug Development

Translational medicine is that branch of medical research, which endeavors to directly connect basic research to patient care (Nagappa 2006). It translates basic research into real therapies for real patients. The bench-to-the-bedside concept links laboratory findings to the patient's bedside for treatment. Many pharmaceutical companies have formed groups that will facilitate the interaction. In the past, obstacles have stood between basic research and clinical practice of medicine. New drugs were developed apart of the clinic and then endorsed for safety-testing and the necessary clinical trials. Translational medicine aims at eliminating the gap and obstacles between them, hence the laboratory "bench-to-bedside" of the patient concept. It is a special diagnostics, which provides information on the prognosis and the patient on condition. It is non-invasive and will not cause discomfort in the course of diagnosis. Translational medicine is a technology, which meets these requirements. It identifies future and probable problems, which surface in the process of drug development and its use. It guides the processes of drug development (Nagappa). Theories evolved in the laboratory bench during pre-clinical experimentation are tested on sick subjects in bed. The results of the test are then taken back to the laboratory for the refining of biological principles on human disease already known (TCG 2008). Traditional medicine is a mix of basic science and clinical research aspects. This mix requires skills and resources traditionally different and separate. This is why research institutes, university science departments, clinics, hospitals and other similar entities cannot just accomplish traditional medicine. Organizations or entities, which possess these resources, encounter internal, political and organizational impediments in integrating the functions and departments. Traditional medicine can operate effectively only in places where experts work together and share information (TCG).

Biomedical Informatics

This covers the fields of bioinformatics, medical imaging, health informatics, and related disciplines (Landers 2010). It began as a scientific discipline in the early 1970s. It peaked in recent years on account of public access to huge data from the Human Genome Project and other research initiatives. The merger of the different branches of biology with information technology and knowledge has allowed researchers and clinicians access to information to advance biological research and healthcare. The integration led to unprecedented breakthroughs in the healthcare and pharmaceutical fields. Activities, such as modeling, identifying DNA sequences, the analysis of protein structures, and data management, can now be performed with minimal effort and amazing speed. Researchers can now acquire massive information and understanding of the human organism and its environment with ease. Data and knowledge covers the entire gamut of molecular exchanges, cell communication, personal genotypes and group populations (Landers).

The best place for biomedical informatics at present appears to be in personalized medical care (Landers 2010). In this field, biomedical information technology can use existing traditional health data in the person's medical records, individual phenotype information and other sources for improved healthcare delivery. Clinicians may also use all the information in detecting developing diseases in early stages. Advancements in biomedical informatics will also enable healthcare professionals to conduct clearer and more sophisticated medical assessments. These assessments can be sent speedily to patients and their healthcare providers. Other close possibilities include breakthroughs in diagnostic and remedial techniques, which will not only improve the healthcare system but also the effectiveness and efficiencies of the industry itself. Proponents of biomedical informatics foresee further consolidation and development for this branch of science. Sustained study and comprehension of vast amount of data are one way. Other possibilities are the creation of innovative algorithms, specialized software, and more automated processes. It can improve personalized medical services and reduce costs through a commitment of the entire healthcare industry. The industry can make wide and effective use of the data, knowledge and computing systems made available by the technology (Landers).

Aspects

Biomedical informatics or BI is a set of methodologies that can cross the barriers confronted by translational medicine (Sarkar 2010). Its fundamental aspects are bioinformatics, imaging informatics, clinical informatics, and public health informatics. With these aspects, BI has essentially improved and facilitated in bringing lab findings to the bedside, evaluate the effectiveness of current interventions, and help assess the impact of translational medicine innovations on health policies. BI and translational medicine together can accrue to better patient care through better clinical interventions and treatments and more informed policies and clinical guidelines (Sarkar).

New Treatments

Traditional medicine ultimately aims at developing new treatments and insights into improving the health of populations (Sarkar 2010). The three steps in the process are the identification of appropriate interventions; directed evaluation of the efficacy of these interventions; and the identification of how to appropriately facilitated and applied to an entire population. Current translational barriers exist in translating or converting innovations from bench-based experiments to clinical validation in bedside clinical trials. These ultimately lead to adoption of communities and the creation of policies. Crossing or overcoming these barriers may be enabled by a combination of present and oncoming biomedical informatics strategies and tools. An important feature of the intervention is a trans-disciplinary team. This team will integrate relevant lab findings in identifying breakthroughs in research and clinical interventions, including drug development. BI professionals, also called biomedical informaticians, will be an essential member of the translational medicine team. They will be experts in effective translating concepts and sharing these with other experts in the team (Sarkar).

Translational bioinformatics and clinical research informatics are both fundamentally knowledge-centered (Sarkar 2010). Both are attuned to meet the vast requirements of research and information of basic science, clinical and public health researchers. The development of knowledge management infrastructures and standards has, through biomedical research, facilitated research into domains like cancer and neuroimaging. BI's ability should be managed towards exploring information on human medicine as its ultimate pursuit. Bioinformatics methods are meant to identify molecular and cellular areas or aspects for specific clinical interventions and better insights into the profile of the disease. Imaging informatics are designed to understand pathogenesis and identification of treatment from the molecular, cellular, tissue or organ level. Innovations are aimed at improving patient care by providing relevant information at the bedside. And public health informatics solutions address population-based needs. These needs may be the tracking of emergent infectious diseases, the development of resources as response; and the evaluation of the latest clinical interventions affecting the population (Sarkar).

Information Needs and Principles of Information Retrieval

Lancaster and Warner (1993 as qtd in Mendonca et al. 2001) identified three basic types of information needs. These are the need to solve a certain problem or arrive at a decision; the need for background information; and the need for updated information on a particular subject. Studies showed that physician information needs center mostly on patients' problems, categorized as diagnosis or treatment. These most often occur in primary care and practice-related rather than generalized. Most frequent questions were on the causes of symptoms, drug dosage and treatment. Nurses' information needs, on the other hand, were mostly patient-specific. These referred to general nursing care, medication administration, and laboratory reports. Their other information needs were for institution-specific and domain knowledge. Other studies revealed that the regular use of computers was only a few times a month by practitioners in hospital units, ambulatory clinics, office-based practices and a clinical pharmacy group (Mendonca et al.).

Evidence-based practice is defined as the responsible, open and proper use of the current and best evidence in making decisions on care (Mendonca et al. 2001). It focuses on diagnosis, etiology, prognosis, therapy, prevention, and other clinical and health care matters. It needs to access, summarize and apply information for daily operations and clinical situations. Doing so involves a process, consisting of four basic steps. These are rephrasing their information needs into clear questions; effectively locating the best evidence to answer the questions; critically validating the evidence and its clinical usefulness; and evaluating its outcome in clinical application (Mendonca et al.).

Information Retrieval

Information retrieval systems are used to organize and obtain relevant information from databases (Landers 2010). This is done when a request is made to the computer system to retrieve the information from the databases. The most popular information retrieval tool in the field of biomedical informatics is the PubMed Interface to the MEDLINE citation database. It not only contains substantial information in biomedicine but is also quite effective in bioinformatics. These systems can be drawn from existing as well as developing approaches within the biomedical information community, such as the contemporary semantic web technologies. That capability to efficiently identify relevant data is crucial in building the knowledge that will cross the translational barriers. This ability has been demonstrated by GenBank and MEDLINE (Landers).

Electronic Health Records

A patient's encounters with the health care system are inputted into medical charts in addition to the data taken down by direct care providers (Landers 2010). These medical charts are now evolved and computerized as electronic health records or EHRs from paper-based medical documents. These are data from ancillary services, like radiology, laboratory and pharmacy. They have become available across health care. EHRs can include varied information constructed by clinicians from the patient's bedside. They can also have electronic interfaces to individual services, such as administrative, laboratory, radiology and pharmacy. Many of these can be used and inputted into the computerized provider order entry or CPOE. Through CPOE, clinicians can electronically order services and tap real-time clinical decision support features. One such feature is a warning about an order that may have adverse effects (Landers).

Overwhelming large amounts of available electronic health data can have significant impact on translational medicine (Landers 2010). Personal health projects, like Google Health and consumer services, can generate more genotype and phenotype data for analysis and support for community-based studies. Massive data proceeding from consumer-driven grassroots effort may produce the next breakthroughs. These and core biomedical informatics retrieval techniques can be used with HER-based data in crossing translational barriers (Landers).

The Biomedical Informatician in the Translational Medicine Team

He has a unique education, with expertise in at least one area (Landers 2010). That

expertise is meant to enable trans-disciplinary team science and needed by translational medicine to succeed. The biomedical informatician is able to interact with key stakeholders across the translational medicine realm. This may consist of biologists, clinicians, clinical researchers, epidemiologists and health service researchers. The biomedical informatician's role is so important that his addition into the team will more than determine its success. That success also depends on the team's understanding of biomedical informatics. With this view in mind, the importance of a biomedical informatics is a key consideration in the entire field of translational medicine. The biomedical informatics expertise infused into the team should also complement the requisite domain expertise. Hence, the biomedical informatician's synergistic relationship with the other team members has become a major challenge in the pursuit of breakthroughs in translational medicine (Landers).

A Model IR Technology

A model was presented to demonstrate these functions. It integrates a querying module, which uses evidence-based principles, with an electronic medical record in a digital library (Mendonca et al. 2001). Its major components are display, data, and processing. It facilitates search, presentation, and summarization of medical information obtained online. It automatically produces queries about the specific characteristics of individual patients. It uses patient information to guide clinicians in the search for evidence for use in patient care. Examples are when a patient is not compliant with his medication and when a comparison is made between two or more medications as to better compliance. There are, however, certain questions arise in the process, such as generalizability of the methods and the model. The authors say it can be generalized according to the quality of medical records/knowledge base, or KB, and the technical aspects of integration. Integration concerns can be settled if resources develops agree on standards, such as set of elements, controlled vocabularies or classification of data elements (Mendonca et al.).

Market

Medical informatics, or MI, has been defined as the use of information science and information technology on the problems confronted by the theory and practice of biomedical research, clinical practice and medical education (Morris 2002). It is the point where biomedicine makes use of products and services evolved by information science and technology. According to MI-related data searches for 1995-1999 on frequently used indexing, information science or technology perspective centers on diagnostic technologies and techniques and data analysis and management. The information science or information technology -- IS or IT -- relates to a service provider, while the biomedical aspect is consumer-oriented. MI, on the other hand, is the marketplace. The market consists of biomedicine's research applications within MI. This covers areas, such as diagnostic imaging, decision support systems and artificial intelligence, biophysics and biomechanics and the design and use of medical computing systems. More specifically, evidence-based medicine, biotechnology, genomics, picture archiving and communication systems, and healthcare administration and management are IS/IT-dependent for its principles, theories and techniques for advancement. Furthermore, biomedicine needs to draw from the knowledge discovery and knowledge management aspects within MI (Morris).

In summary, MI is the meeting point between biomedicine and information science and information technology (Morris 2002). Biomedicine needs tools to achieve its goals. IS/IT provides these tools in addition to principles and theories to market biomedicine. IS/IT products are applied specifically on evidence-based medicine, the center of MI (Morris).

Other Uses of Informatics in the Health-Related Areas

The expanding application of medical or clinical informatics can go beyond bioinformatics, imaging informatics, specific health care disciplines -- such as nursing and dentistry -, research and public health (Hersh 2009). It can extend to consumer health informatics or informatics from a consumer's point-of-view. It is also now creating an entire health information management system. Health information management or HIM is a discipline, which focuses on the management of medical records. This comes as a consequence of medical records' becoming electronic. HIM differs from informatics in that HIM is used for education while informatics is used by clinicians (Hersh).

EMR, PHR

Computerized individual health records were first referred to electronic medical records or EMR (Hersh 2009). It has been replaced by electronic health records, which comprise a broader and more varied collection of patient information. The personal health records or PHR format is also evolving. This is the patient-controlled aspect of health records in which the patient can interact with his clinician securely. Through these, the patient can access the clinician's working records and become part of the care team. He does this by suggesting revisions on his data and keeping tab of his own progress with the clinician (Hersh).

HIE

Health information exchange is an exchange of information on patient care from many points of business boundaries involved in health care (Hersh 2009). This exchange is generally operated by a regional health information organization, or RHIO. Even health care organizations with excellent HIT systems have problems providing patient information to other groups where patients receive care. People are much more mobile today than before. Their data must, therefore, follow them. HIE is viewed as the secondary or re-use of clinical data when used for other applications, like quality assurance, clinical research, and public health (Hersh).

Tele-Specialties

Telemedicine is the delivery of health care when the participants in the exchange are separated by time or space (Hersh 2009). Tele-health, on the other hand, focuses on the direct interaction with health on information and communication technology. Other specialties can be tele-radiology, tele-pathology and eHealth. The concept of eHealth consists of health and technology and minor aspects, such as commerce, activities, stakeholders, results, place and perspectives (Hersh).

Comparative Effectiveness Research

Evidence-based medicine needs to be understood as the practice of medicine and decisions made according to the best scientific evidence available (Hersh 2009). The decision is drawn from the context of patient, clinician and social constraints. Newly evolving is comparative effectiveness research. It evaluates and compares clinical outcomes, effectiveness, risk and benefits of medical treatments and services addressing a specific medical condition. The Academy Health Methods Council describes it as a collection of studies comparing diagnostic or treatment options in evaluating their effectiveness, safety and outcomes (Hersh).

Assurance from the FDA

The U.S. Food and Drug Administration announced a plan to upgrade the agency's "regulatory science" with a requested $25 million budget by President Barack Obama

(Lowes 2010). It will modernize the scientific assessment of drugs, biologics and medical devices it regulates and speed up the marketing of the good ones. In a press conference in Washington, FDA Commissioner Margaret Hamburg said that regulatory science has life and death implications. At present, she said discoveries in biomedical research are slow in reaching patient care on account of outdated 20th-century methods used in evaluating 21st-century science. There is a shortage of new antibiotics at a time of acute antibiotic resistance, to illustrate her point (Lowes).

Commissioner Hamburg stressed the need for new and better drugs at present, a time when research and development are at a low level (Lowes 2010). The range of new antibiotics is alarmingly limited in type, class, availability and the diseases they are supposed to treat, according to her. This was the same message driven by the 2007 report of the FDA's Science Board. It said that the agency has been confronting serious deficiencies and has not been able to meet its regulatory responsibilities. It said that in the face overwhelmingly revolutionary change in drug discovery and development, FDA's evaluation methods have remained in 20th-century levels (Lowes).

Under this new plan, the FDA will consider new and sophisticated technologies, which will help it protect food supply, regulate tobacco, improve children's health, curb emerging infectious diseases and bioterrorism and accelerate the approval of new treatments (Lowes 2010). One challenge is the lack of infrastructure and tools for the regulatory science of bioinformatics. FDA ironically has the largest repository of clinical data on drugs, biologics, and medical devices, before and after approval. It hopes to create a common database for this vast collection of information. The database is designed for querying by topic and analyzed for answers to questions on a medical product's efficacy or safety. In this scenario, for example, FDA can review 15 studies of HIV drugs at the same time, compare and identify which ones work best and for what kind of patients. Pursuing this objective will require investments in informatics hardware and software (Lowes).