Personalized Medicine and Biomarkers

Personalized Medicine and Biomarkers

• Biomarkers have become increasingly crucial in personalized medicine, aiding in diagnosis, prognosis, and targeted therapy selection.
• Biomarker applications span a wide range, including pharmacodynamics and treatment monitoring.

Categories of Biomarkers

• DNA biomarkers, DNA tumor biomarkers, and general biomarkers comprise three broad categories.
• Clinical trial phases for identifying and validating diagnostic/prognostic biomarkers are outlined.
• Companion diagnostic tests, or predictive biomarkers, foresee treatment response regarding efficacy/safety.

Genetic Variation and Personalized Medicine

• Genetic variation influences both disease susceptibility and treatment response.
• Genome-wide association studies (GWAS) have accelerated the discovery of genetic variants contributing to complex diseases and pharmacogenetic markers.
• Personalized medicine aims to enhance medical decision-making through genetic epidemiology.

Personalized Medicine Applications

• Personalized medicine identifies optimal drugs and dosages for specific patient subgroups.
• Applications include withholding treatment, preventive interventions, and targeted treatment options.
• DNA biomarker tests in prostate cancer can determine whether treatment can be safely delayed through watchful waiting.
• This is applicable if a tumor lacks aggressive-form-causing genes, potentially remaining stable for decades and eliminating the need for radical resection and subsequent therapies, potentially eliminating the need for radical surgical resection and subsequent radio- or chemotherapy (Kroll 2008).
• Genetic profiles guide preventive interventions for hereditary cancers, using genetic testing to decide on specific, sometimes radical, interventions like preventive surgery (Kroll 2008).
• Personalized medicine offers targeted treatment options for individual patients, beyond the large subgroup treatment schemes, which are termed stratified medicine (Trusheim et al. 2007).

Anti-Inflammatory Therapies

• Anti-inflammatory therapies (anti-TNF, anti IL-6, or anti IL-1β) can treat inflammatory diseases such as Crohn's disease (Buchner et al. 2011; Cottone et al. 2010).
• Physicians can sequence the genetic profile of a patient to choose an efficient anti-inflammatory therapy, or a combination.

Diversity in Definition

• A biomarker is any measurable substance or biological structure in the human body that may influence, explain, or predict the incidence or outcome of disease.
• Biomarkers are any substance, structure, or process measurable in biospecimen associated with health-related outcomes per Gallo et al (2011).
• A biomarker is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention according to the Biomarkers Definitions Working Group (2001).

Prognostic vs. Predictive Biomarkers

• Prognostic biomarkers predict disease progression.
• Predictive biomarkers correlate with treatment response.

DNA Biomarkers

• DNA biomarkers represent the stability of genetic information by directing protein production for cell structure and function over a lifetime.
• Single nucleotide polymorphisms (SNPs), short tandem repeats (STRs), deletions, insertions, or other DNA sequence variations encompass them.
• SNPs are the most common type because of high-throughput molecular biological facilities, commonly resulting in three possible genotypes due to diallelic nature.

DNA Tumor Biomarkers

• Cancer involves DNA changes at the cellular level, measureable in the tumor.
• These are specific to cancerous tumors, usually determining the presence or absence of a gene mutation.

General Biomarkers

• RNA, protein, or metabolite measurements from biofluid, tissue, or cell lines are described as general biomarkers.
• Most provide quantitative assessments with positive measurement values.
• DNA and DNA tumor markers are discrete.

Thresholds in Diagnostic Processes

• Thresholds are essential or all types of biomarkers in the diagnostic process to relate biomarker measurements to clinical decision-making.

Stability of DNA

• DNA maintains overall stability, being reproducible and measurable at anytime, facilitating use in prospective and retrospective studies.
• DNA biomarkers can be validated prospectively in biobanks, but the authors do not consider this type of study to be prospective

DNA Measurement

• DNA biomarkers are simpler to measure with lower laboratory time and cost as sample drawing, handling, and storage protocols are more simple.
• DNA biomarkers are unsuitable for therapy monitoring, pharmacodynamics, or surrogate markers because of stability over lifetime.
• Multimarker discovery rates are frequently more rapid than product cycles, meaning DNA multimarker tests may face scientific obsolescence despite marketing approval, as the discovery rate of new DNA biomarkers outpaces their product cycles..

Terms

• Diagnostic biomarkers relate to disease diagnosis or severity.
• Screening biomarkers are the most important diagnostic biomarkers, to discriminate between healthy individuals and those in an early disease stage.
• Prognostic biomarkers help predict the likely course of disease in a defined clinical population under standard treatment conditions if a diagnosis is known.
• Predictive biomarkers predict a patient's response to a particular treatment in terms of efficacy and/or safety, supporting clinical decision-making.
• MammaPrint®, a DNA tumor biomarker guides physicians in therapy by indicating whether breast cancer metastasis risk is high or low following surgery.
• MammaPrint® has FDA clearance as an in vitro diagnostic multivariate index assay (IVDMIA) (Slodkowska and Ross 2009).
• Some health insurance companies in Germany covered costs for some particular cases in 2012.
• GWAs have shown that the IL28B gene strongly indicates response to standard hepatitis C virus-1 (HCV-1) treatment (Holmes et al. 2011).
• Population stratification exists in the IL28B gene, affecting the treatment response across ethnic groups.
• Caucasians with the "good response" genotype are more likely to benefit from treatment, as noted in the Provisional Guidance for HCV treatment (Thomas et al. 2012). This states the lack of efficacy of special inhibitors in persons with "non-response" genotype, or African ancestry.

Diagnostic Biomarker Studies

• The drug development process consists of four phases.
• Regulatory approval is essential before market entry, with pre-clinical programs testing safety, pharmacology, and pharmacodynamics in animal models.
• Three clinical phases establish pharmacokinetics, pharmacodynamics, dose, clinical efficacy, and benefit/risk. Effects on morbidity/mortality become explored in the fourth phase (European Medicines Agency 1998b).
• Diagnostic test development programs undergo a similar process (European Medicines Agency 2009). Phase I demonstrates the safe application of the diagnostic test to humans and that the technical properties (application mode, reproducibility, etc.) are well understood.
• Phase II applies the test on either diseased or "healthy" individuals to begin sensitivity and specificity estimates. Phase III conducts validation studies, to carry out the diagnostic test/workup in the same manner/setting as clinical/diagnostic practice.
• Phase IV diagnostic studies investigate an improvement of clinical outcome in a broader population, and whether the application of the test leads to a measurable improvement of the clinical outcome in a broader population.

Extended Phase Approach

• The extended phase approach includes discovery (Ia), assay development/validation (Ib), and retrospective validation (Ic), being helpful from a developmental perspective for diagnostic/prognostic biomarkers.
• Phase Ia uses high-throughput technologies like GWAs, genome-wide meta-analysis, or whole genome sequencing study for DNA, and expression clone or multiplex protein antigen arrays in proteomics.
• Phase Ib adjusts identified biomarkers to a laboratory setting integrated to clinical routine, reliable and precise, combining assay transfer to a better-suited laboratory platform and the choice of DNA marker(s)
• There is development ELISA in proteomics, such as the ELISA for AGR2 in voided urine for detecting secreted prostate cancer (Wayner et al. 2011) as well as the complementary technology introduced in this phase is generally more reliable and precise.
• Phase Ic, a first retrospective validation, is performed to determine whether the results still hold true, being performed with a higher sample size than the initial throughput search

Multimarker Model Development

• Multimarker models get developed, either as part of phase Ic, with the initial patient cohort, or in phase II, in a retrospective study using different patients from those of phase I.
• These models, wether initially developed in phase I or II, always undergo refinement with the second group of patients
• Phase III (a prospective investigation), obtains reliable estimators for sensitivity and specificity of DNA biomarker tests and for attaining reliable estimates of the prognosis like through the use of Brier scores.
• Phase IVa is a randomized controlled trial, following marketing approval showing treating doctors knowledge leads to improve patient management and demonstrate improve patient outcome.
• Phase IVb are health economics studies to assess cost-effectiveness involving a randomized controlled trial which mimics the development of predictive biomarker.
• a challenging element of this extended phase model for diagnostic/prognostic biomarkers is the term "prospective validation" for phase III as germline DNA is assumed stable.

FDA Draft Guidance

• The FDA issued a draft guidance for industry for "in vitro companion diagnostic devices" in July, 2011, noting they are predictive biomarkers essential for therapeutic product safety and effective use.
• The anticipated companion diagnostics are used: to identify (1) patients most likely to benefit, (2) patients with increased risk for therapy adverse reactions and (3) treatment responses and appropriate treatment adjustments.
• Diagnostic tests refine the patient population if treatment effects or tolerability varies, where DNA and tumor biomarkers can't be used for treatment monitoring, but can be used for steps 1 and 2 from above.

Biobanks

• The use of biobanks for prospective validation are possibly helpful for already approved drugs, if biomaterial shows systematic investigations into improved efficacy or safety in biomarker-defined subgroups.
• Pharmaceutical industry research is going to be more important for pharmacovigilance and post-marketing surveillance of drug use, though there are no valid conclusions if using so-called convenience samples for genotyping purposes.
• To be considered as such the diagnostic must show efficacy and benefit/risk in the experimental. For example at the end of the development program, if the treatment has comparable positive effects in test-negative patients as in test-positive patients, a diagnostic test would not be required.

Tests

• Diagnostic tests are only valuable if data and/or tests have larger clinical data.
• One should note ethical reasons to support that treatment be withheld in test-negative patients.
• Official regulatory guidance about appropriate study to validate companion diagnostics together with new drugs is still limited, despite the FDA and EMA having established procedures for negotiating required evidence for authorization.

Study Design

• In selection designs, patients are first tested with the companion test.
• Those test-positive are then randomized to treatment or control but Buyse et al. (2011) note that these selection designs provide clear information about treatment efficacy, but do not show lack of benefit in marker-negative patients; a selection design is only of limited value.
• Testing all incurs costs, and for appplication to be qualified, the design should thus quuantify its uselfuness with value limited otherwise.
• Designs stratified for Interaction or Biomarker first test patients for the biomarker, and then randomize treatment with control having been stratified with a test result.
• This study is a potential gold-standard, providing rationale decision making about potential biomarkers.

Biomarker in Population

• Where testing was only one parameter to consider, Adaptive study designs offer a promising means to stop recruitment for futility in test-negative patients.
• No gold standard may mean a diagnostic test could be made available by with a test that can't be measured, to show a difference with a more accessible treatment selection, not requiring that both biomarkers are understood in each combination
• Designs tested across a large number of different treatments are easily validated as a form of "strategic trial"

Data & Safety

• The way data is collected will not affect the validity, with designs needing reflection data to support patient management to give that level of support.
• The complexity can be found where a high drug efficacy is required to meet various challenges

Diagnostics

• Diagnostics and therapy stem from three pillars of a physician's work, which will show identical results if used right and often
• Those tests, especially DNA results will support future studies.
• The power the test lends has the potential to reduce future treatments without testing.
• As noted through data analysis is the validity that will give better treatment to the proper population.