|Year : 2018 | Volume
| Issue : 2 | Page : 245-248
Translational research in oral cancer: “A challenging key step in moving from bench to bedside”
Neha Sharma, Rajeshwari G Annigeri
Department of Oral Medicine and Radiology, College of Dental Sciences, Davangere, Karnataka, India
|Date of Web Publication||8-Mar-2018|
Dr. Neha Sharma
Department of Oral Medicine and Radiology, College of Dental Sciences, Davangere - 577 004, Karnataka
Source of Support: None, Conflict of Interest: None
This review summarizes and interprets the rate of improvement in cancer treatments which have remained frustratingly slow despite considerable investment in oncological research. Hence, this review emphasizes on minimizing exposure of patients to ineffective investigational therapies by decreasing the number of poorly designed clinical trials through stronger collaboration between industry and academia which can be done by multidisciplinary teams who are essential to facilitate future cancer outcome studies focused on improving clinical care of cancer patients and implementing effective interventions to ultimately improve the quality and duration of survival. Hence, it is recommended to explore all these factors which contribute toward translational lag between implementation of discoveries from basic research into clinical use.
Keywords: Biomarkers, personalized medicine, translational research, tumorigenesis
|How to cite this article:|
Sharma N, Annigeri RG. Translational research in oral cancer: “A challenging key step in moving from bench to bedside”. J Can Res Ther 2018;14:245-8
| > Introduction|| |
Cancer is responsible for one in eight deaths worldwide, with more than 12 million new cases diagnosed yearly. Despite the fact that tumorigenesis has been understood at molecular level, the death rate has not lessened over the past couple of decades. High mortality due to oral cancer in India can be accredited to various factors such as genetic vulnerability, widespread use of tobacco, environment, and life-style. Also, allied factors such as genetic polymorphism among patient population, evolving heterogeneity among cancer subtypes, and lack of prefect disease model make management of oral cancer very challenging task. Even after carefully designed trials funded by the encouraging preclinical results, most of the drugs fail later during clinical trials. These late-stage failures are mostly due to absence of animal model which can perfectly simulate human physiology. Working in silo more often creates a wide gap between basic researcher and clinical researcher. All these factors contribute toward translational lag between implementation of discoveries from basic research into clinical use.
Although translation has been discussed for more than 30 years, the process recently has become a major focus in biomedical research. Translational research in oral cancer is highly desired to come up with better diagnostics and therapeutic applications to improve such lifeless picture creating high frequency of deaths by oral cancer.
| > Understanding Translational Research|| |
Translational research brings up the multidirectional combination of basic research, patient-oriented research, and population-based research, with the enduring aim of improving the health of the public. The translational archetype has been subdivided into four steps: T1 research seeks to move a basic discovery into a candidate health application; T2 research assesses the value of T1 application for health practice leading to the development of evidence-based guidelines; T3 research attempts to move evidence-based guidelines into health practice, through delivery, dissemination, and diffusion research; and T4 research seeks to evaluate the “real world” health outcomes of a T1 application in practice.,
Full-spectrum biomedical research comprises essential components such as translational and clinical research. However, these grave arenas of research are hindered by increase in costs and complexity, a scarcity of information systems, and increase in the regulatory burden. An explosion in clinical-service demands, and reduced financial margins have sharply cut the research time for many clinical and translational researchers and weakened the time and attention devoted to the research mission of academic institutions which has inevitably resulted in difficulties in the retention of human subjects in clinical trials and, ultimately, considerable delay in the completion of critical studies.
As a matter of fact, the drugs should be approved for their effectiveness and the real benefits they bring to the society, so only with well-conceived and justified clinical trials and taking advantage of noninvasive monitoring techniques such as molecular imaging, we can decrease the drug development expenditures, and ultimately the cost of health care. Complex clinical trials with a strong targeted translational research component enable fundamental advances in the understanding of particular cancer types and directly contribute in defining new personalized standards of patient care. Hence, with the use of proper designs, new drugs or combinations of drugs in multiple cancer entities can be investigated by conducting clinical trials, involving the survey and qualification of biomarker(s) leading to an improved understanding of the biology of the disease, and incorporating molecular characterization of tumors which could be predictive of activity or toxicity.
The novel challenge in cancer drug development is not only to prove the safety and efficacy of the drug but also to determine how to achieve this in a rapid and cost-effective manner. Teamwork of epidemiologists, clinicians, biostatisticians, and bioinformaticians needs to collaborate to apply arduous methods across study designs with the focused intent to improve clinical care of cancer patients and identify those at high risk of poor outcomes to implement effective interventions to improve prognosis, quality of life, and overall health.
| > Available Tools|| |
The translational research tools that are currently and commonly being used in the scientific community are translational biology, predictive toxicology, in vitro–in vivo extrapolation, quantitative pharmacology, biomarkers, and surrogate endpoints. Advances in understanding of biologic systems and the development of powerful new tools that can be applied at both the bench and the bedside are genomics, proteomics, transgenic animal models, structural biology, biochemistry, and imaging technologies that offer unparalleled prospects for advancing knowledge of human disorders in a translational context. Sawyers points out that 'Human subjects are an essential early component in the evaluation of new drug candidates and should be studied at a level of scientific detail comparable to that used for nonhuman preclinical model systems.' The ultimate goal of translational medicine is enabling personalized care. Nowadays efforts are made to populate translational research platforms with patient data to fuel discovery.
| > Barriers to Effective Translational Research|| |
There are three major obstacles to effective translational medicine. The first being the challenge of translating basic science discoveries into clinical studies, the second hurdle is the translation of clinical studies into medical practice and health care policy, and the third obstacle to effective translational medicine is also philosophical. There are many obstructions to successful bench to bedside research, but few have been acknowledged as the “valley of death.” The valley of death states that there is lack of funding and support for research which moves basic science discoveries into diagnostics, devices, and treatments in humans. The valley of death is also present because bridging the translational gap is dependent on following risk factors:
- Complexity of research with human subjects regulatory issues, human subject protection, intellectual property issues, lack of funding, fragmented infrastructure, shortage of trained investigators, and shortage of resources for including sufficient patients
- Goal-oriented, high-risk, team research is difficult to sustain in academic settings
- Lack of focus on key high-risk translational barriers and opportunities
- Limitation in understanding oncogenesis and lack of identification of key molecular targets
- Need for new clinical trial designs appropriate for predictive personalized medicine.
The lack of adequate framework prevents the identification of steps during which research and knowledge are ''lost in translation'' and therefore do not reach public health gains. Structural barriers include the pressures of clinical service delivery, which have left many hospital- or practice-based clinicians with insufficient time for research.,,, Many of us harbor an uneasy but quite a realistic suspicion that the gap between what we know about diseases and what we do to prevent and treat them will become wider even if the ever-growing scientific knowledge and the additional new discoveries that are likely in the future are given. Moreover, it is not just recent research results that are not finding their way into clinical practice and public health behaviors and there is plenty of evidence that “old” research outcomes have been lost in translation as well.
Cognitive barriers are the unique research design which translates research generated in the highly controlled laboratory environment into the relatively chaotic and complex clinical environment. This may assimilate the use of experts such as biostatisticians to interpret results and information technology specialists to design and run more complex computer algorithms for integrated analyses. Lack of standardization of clinical trial protocols, data recording, and evidence thresholds are also cited as barriers. Inability to access results from previous trials or research programs, especially those that have not reached publication, also hinders translation.
| > Future Strategies|| |
Advances in translational research are expected to enable the development of safe and efficacious drug therapies in the 21st century. Despite significant progress recently made in biological sciences, the results are decidedly mixed. Continuing on this journey, a review of successes and hurdles could help identify the areas of needs and strategies.
Small studies are especially sensitive to the effects of missing data; the inclusion or exclusion of a single data point can alter the results and conclusions of a study. Thus, a prospective plan to deal with missing data and outliers is essential. Animal deaths and the handling of missing data along with outliers should be discussed with the study results, where applicable. Multiplicity in statistical testing is often an issue in preclinical studies that are inadequately addressed. Clinically, the current approach of treating broad populations of patients using a singular approach is not well suited to the development of molecularly targeted drugs. Although developing drugs with individual patient focus are immensely more complex, it should improve the success rate of development, as well as provide benefit to patients and ultimately to the economics of healthcare.
The concept of personalized medicine refers to the fitting of medical treatment to the individual characteristics of each patient and to classify individuals into subpopulations that differ in their predisposition to a particular disease or their response to a specific treatment so that preventive or therapeutic intervention can be concentrated on those who will benefit, sparing expense and side effects for those who will not, and this concept is increasingly impelling health care. In future, disease will be based on the underlying mechanisms at the molecular biological level instead of being classified based on symptoms or according to the organ system. The molecular method could facilitate personalized medicine through the use of various biomarker tests on gene expression, proteins, and metabolism. A companion diagnostic test, especially a biomarker test, allows knowledge-based decisions in therapeutic drug development and could help improve the safety and efficacy of the drug. Ideally, basic biomarker research should start at least 2 or 4 years before first-in-man clinical trials, and once having embarked on the clinical trials route, it is advisable to continue the biomarker research in parallel with the clinical development program. The concept of personalized medicine could potentially reduce the use of drugs in nonresponders, but it may increase diagnostic budgets by requiring the testing of a whole patient population to identify groups of responders, and only smaller groups of eligible patients might benefit; therefore, it leads to higher unit prices. Bench scientist can be warned and helped by clinical researcher to develop patient-centric therapies by making them aware of practical issues which are not encountered in animal studies. Such close cooperation would also enable refinement of theoretical disease models by including significant parameters from clinical observations and these models can be used to disease diagnosis, target discovery, and safety and efficacy evaluation. Utilizing such theoretical models will help in optimizing effort, time, and cost involved in different phases of drug discovery. Natural compounds with medicinal values are used in India from time innumerable. Translational research focused on use of active compounds from these natural sources should be attempted to design therapeutic interventions for potential targets present in ethnic community. Translational research will also require large-scale collaboration among vibrant scientific community working toward common objective of reducing mortality, incidence rate for oral cancer. Strong policy level commitment from Indian government in the form of rationalizing tobacco use, promoting translational research, spreading community awareness will go long way in saving millions of lives on stake due to oral cancer.
| > Recommendations for Future Clinical Research|| |
We are transitioning from an empirical approach (large trials comparing treatments) to a tailored approach (trials asking biologically relevant questions). To be successful in this transition, we need a profound restructuring of clinical research methodology and infrastructure so that we can better understand the biology of the disease and the mechanism of action of new agents, develop new methodological approaches, and document molecular determinants whether host- or tumor-related predictive of toxicity or activity. Therefore, prior assay validation is needed for integrating biological tests. The availability/quality of material is also crucial for biomarker test, and screening platforms are required with large tissue and data collections.
Studies of cost-effectiveness of personalized medicine are still on the way, but promising results have been demonstrated. Good communication between drugs developers, academia, regulatory agencies, and payers is important. Identifying biomarkers is a collaborative effect. It is time-consuming, costly and difficult to identify and validate biomarkers because it requires adequate evidence of clinical utility, testing in a multicenter setting and across different cohorts.,,, Public-private partnership should be encouraged so that the pharmaceutical industry and academia can join forces and generate high-value clinical data in a precompetitive setting. Academic organizations can contribute with scientific advice and incorporate additional translational research projects, and industries could support the infrastructures of screening platforms and/or patient-derived translation research platforms. Such platforms set up by academic networks can secure efficient public-commercial cooperation and avoid duplication of costly screening initiatives by multiple companies. Adaptation of industry-academia interactions is necessary to enable a quick, cost-effective and safe development of new personalized drugs.
| > Conclusion|| |
Thus, the aim is to stimulate the development of a brighter vision for translational and clinical research to ensure that these disciplines remain powerful engines of creativity. Hence, the opportunity should be offered for change to those who share a vision and commitment to innovation and experimentation.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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