The Use of Biomarkers to Guide Precision Treatment for Tobacco Use

Scott Siegel^*
*Correspondence: Scott Siegel, Department of Health Care, University of Cancer Center & Research, New York, USA, Email:

Keywords

Tobacco treatment • Precision treatment • Nicotine metabolite ratio • Biomarker • Smoking cessation

Introduction

An overview of the development of biomarkers for individualized tobacco treatment is provided in this review. We center around biomarkers connected with the treatment of ignitable tobacco with Food and Medication Organization supported tobacco use prescriptions. Other papers in this Special Issue take into account research on imaging, metabolomics, proteomic, genetic, and epigenetic biomarkers. We start with a rundown of the most recent proof from randomized clinical preliminaries of the viability of FDA-supported meds for tobacco use. The promise that biomarkers hold to improve medication treatment responses by increasing efficacy and decreasing side effects is discussed next. The state of the science surrounding biomarkers for tobacco treatment is then summarized within the FDA's conceptual model of biomarker development. Last but not least, we talk about obstacles to clinical implementation, issues with biomarker measurement, special populations, and wider applications of biomarkers in tobacco treatment.

Description

There are presently seven FDA-endorsed drugs to treat tobacco use: three types of NRT available over the counter two prescription NRT medications and two prescription medications that do not contain nicotine. In view of an efficient survey of the proof, the US Preventive Administrations team gave a Grade A suggestion for clinicians to evaluate all grown-ups for tobacco use and give conduct mediations and pharmacotherapy to suspension. Cochrane Reviews with high-quality evidence supporting all forms of NRT, bupropion and varenicline were included in the data. In contrast to those who attempted to quit smoking without treatment, 18 people who currently smoke would need to be treated to avoid an excessive number of deaths from smoking. For setting, a NNT of 18 looks at well to other usually utilized avoidance based types of pharmacotherapy. In addition, treatment for quitting smoking is cost-effective in both clinical and community settings [1].

Given the substantial variation in treatment response and differences in safety, adherence, and financial costs, there is a compelling need to identify methods to match tobacco treatment-seeking individuals to the optimal medication. Identifying individual characteristics that predict a more favorable therapeutic response and reduce side effects to a specific medication would likely improve treatment adherence, reduce spending on ineffective treatments, and improve health outcomes [2]. Toward that end, a range of demographic, clinical, and psychological characteristics have been examined as potential variables to guide the selection of optimal tobacco treatments. This body of work has produced largely inconsistent results. However, significant improvements in understanding of the neurobiology of nicotine addiction have resulted in a cataloguing of novel characteristics that may be associated with response to treatments. Moreover, landmark advances in understanding the human genome have led to tailored treatments with improved outcomes and reduced toxicities for a variety of medical conditions beyond tobacco use. Emerging from these areas of research is the core concept of a predictive biomarker, defined as a characteristic of a person that can be identified prior to treatment and used to select a specific therapeutic, does, or duration of therapy to improve efficacy and/ or reduce treatment toxicity [3].

The FDA's three-phase approach is the foundation for one widely used framework for biomarker development and application: Clinical Application, Development and Discovery during the Discovery phase, various bio-samples are typically used to evaluate the reliability and validity of biomarker assays, which are developed using potential biomarker candidates. At this stage, common indicators of biomarker potential include sensitivity and specificity, as well as negative and positive predictive value. Verification of-idea studies are normal, like those looking at the relationship among biomarkers and clinical elements or aggregates. These steps have included examining measures of dependence, withdrawal, and treatment response in tobacco treatment. While there has been significant headway made in decreasing the general pace of tobacco use in the US throughout the course of recent many years, with the accessibility of protected and compelling prescriptions as a significant explanation, this achievement has to a great extent slowed down and a few sub-bunches inside the US keep on having alarmingly high paces of tobacco use. All the more as of late, propels in how we might interpret the neurobiology of tobacco reliance and the human genome have "made the way for" the chance of customizing medicines utilizing biomarkers [4].

The application of the NMR in the context of lung cancer screening is yet another potential subject for investigation. Based on a review of the evidence demonstrating that LDCT screening can reduce lung cancer mortality, the USPSTF recommended annual low-dose computed tomography screening in high-risk adults in 2013. However, false-positive results that necessitate invasive procedures are one way in which LDCT screening can contribute to patient harm. As a result, shifting the balance between the benefits and harms of lung cancer screening could be beneficial. Arising proof recommends that the NMR predicts openness to cancer-causing agents and poisons embroiled in the improvement of cellular breakdown in the lungs and other smoking-inferable disease, even subsequent to adapting to cigarettes each day and pack years. As a result, it's possible that LDCT and NMR results could be combined in a risk prediction model to cut down on false-positive results. Given physician concerns about false-positive rates, this could increase the uptake of LDCT and, by extension, the opportunities to offer tobacco treatment to patients who are eligible for LDCT but currently smoke. In addition, this would result in improved outcomes [5].

Conclusion

Last but not least, treatment for tobacco use could be tailored by combining multiple biomarkers. The NMR's potential integration with the other biomarkers discussed in this special issue, such as genetic and imaging biomarkers, requires further investigation. Combining biomarkers that reflect the various dimensions of tobacco dependence like emotional, cognitive, and physiological pathways. They may assist in determining more efficient methods for individualized treatment.

Acknowledgement

None.

Conflict of Interest

None.

References

1. Strimbu, Kyle and Jorge A. Tavel. "What are biomarkers?." Curr Opin HIV AIDS 5 (2010): 463.

Google Scholar, Crossref, Indexed at

2. Lucignani, Giovanni. "Imaging biomarkers: From research to patient care—a shift in view." Eur J Nucl Med Mol Imaging 34 (2007): 1693-1697.

Google Scholar, Crossref, Indexed at

3. Massoud, Tarik F. and Sanjiv S. Gambhir. "Integrating noninvasive molecular imaging into molecular medicine: An evolving paradigm." Trends Mol Med 13 (2007): 183-191.

Google Scholar, Crossref, Indexed at

4. Dickerson, Bradford C. and Reisa A. Sperling. "Neuroimaging biomarkers for clinical trials of disease-modifying therapies in Alzheimer's disease." Neuro Rx 2 (2005): 348-360.

Google Scholar, Crossref, Indexed at

5. Chertkow, Howard and Sandra Black. "Imaging biomarkers and their role in dementia clinical trials." Can J Neurol Sci 34 (2007): S77-S83.

Google Scholar, Crossref, Indexed at