The Forces Driving the Future of Personalized Medicine

Transport yourself 10 to 15 years from now and try to imagine what the future of personalized medicine will look like.  The vision of every drug prescription decisions being driven by a test aimed at tailoring the treatment to a particular individual is probably utopian.  Rather, I would argue that the realm of personalized medicine will still be limited to the treatment of severe and/or life-threatening diseases that require expensive medications.  Under this premise, what are the forces that will shape the future of personalized medicine?

In my mind, this question can be addressed by considering the field from a supply and demand perspective.  On the supply end, the pharmaceutical and diagnostics industries will remain the main forces driving the future of personalized medicine.  The imperative of improving the return on investment in drug development will dominate the future of the pharmaceutical industry.  With the era of relying mainly on “one-size-fit-all” drugs fading away, the focus will shift towards precision/personalized medicine where drugs are designed to address the need of smaller targeted patient populations.  Hence, the need to develop the tools that will identify the right patient population (for efficacy and/or safety reasons) will constitute a major theme in drug development.  This does not exclude the continuing effort of the pharmaceutical industry to develop and commercialize broadly applicable drugs for the management and/or treatment of conditions for which a personalized approach is not warranted (for cost-benefit and/or clinical utility reasons).  Still on the supply end but with an eye on the demand side, the regulatory authorities will continue to play a major role in the harmonization of the biomarker and companion diagnostic development process.  Beyond the current regulatory framework governing the regulatory approval of drugs and companion diagnostics, the regulators have been working on developing a new process for an integrated development of biomarkers intended to become companion diagnostics (see earlier posts: Harmonization of Biomarker Qualification Regulatory Submissions; Companion In Vitro Diagnostics (IVD) Development).

Probably the most significant force that will shape the future of personalized medicine will be on the demand side, represented by the patients, the medical practitioners, and most importantly the health insurance/payers.  For all three entities, the adoption of a new companion diagnostic will require proof of clinical utility (Ref1, Ref2, & Ref3).  In a nutshell, clinical utility for a molecular diagnostics is the third level of a three-tiered evaluation framework that includes “analytical validity”, “clinical validity/qualification”, and “clinical utility” (Ref2).  Hence, clinical utility encompasses the overall medical impact of a diagnostic.  A diagnostic is considered clinically useful if it provides a real and substantial advantage to the patients, positively alters the practice of medicine, and/or improves the cost / benefit equation for a given treatment.  Although clinical utility is a distinct concept from analytical and clinical validity, it cannot be established without first establishing the latter.  The reciprocal is however not true: establishing analytical and clinical validity does not imply proof of clinical utility.

While the pharmaceutical industry and the regulators are currently focusing most of their efforts on defining and implementing the rules of diagnostics analytical and clinical validation, I would argue that the next decade will be dedicated to the third part of the equation: defining and implementing the rules of diagnostics clinical utility evaluation.

Thierry Sornasse for Integrated Biomarker Strategy

A Clinically Qualified Biomarker of Response to anti-CD20 Therapy in Rheumatoid Arthritis

In the September 21^st issue of Science Translational Medicine (link), scientists at Genentech reveal their findings about a new, clinically qualified biomarker of non-response to antibody therapy to CD20 in rheumatoid arthritis (RA).  B cell depleting therapy using the anti-CD20 mAb rituximab in RA (link) is reserved for patients who have failed standard disease-modifying antirheumatic drugs (specifically methotrexate) and/or with inadequate response to anti-TNF antibody therapy.  Considering the cost, complexity, and relative risk associates with anti-CD20 therapy and considering that about 50% of RA patients do not respond to rituximab, there is a strong impetus to target this therapy to patients who are most likely to respond favorably.

Hypothesizing that RA patients with high frequency of antibody producing plasma B cells are less likely to respond to rituximab (plasma cells do not express CD20), the team at Genentech surveyed a set of B cell and plasma cell specific RNA transcripts in blood samples from a subgroup of patients who had been treated with rituximab (REFLEX study).  Using the American College of Rheumatology 50% improvement criteria (ACR50), they identified a clear association between failure to meet ACR50 and elevated levels of RNA for the immunoglobulin J chain (IgJ) at baseline.  They confirmed this observation using blood samples from patients enrolled in two additional independent rituximab studies (DANCER and SERENE), and one study of ocrelizumab (a second generation anti-CD20 mAb) in RA (SCRIPT).  When all four trials were combined, the ACR50 response rate in the active arms was 28% for the IgJ^lo group (n = 471) and 12% for the IgJ^hi group (n = 122) (Odd ratio: 2.7; 95% confidence interval: 1.5 to 5.3).  The predictive power of the IgJ RNA level was further refined by combining this parameter with the RNA levels for the B cell specific splice variant of Fc Receptor-like 5 (FCRL5).  Together, elevated levels of IgJ RNA and low levels of FCRL5 at baseline (biomarker positive: IgJ^lo / FCRL5^hi) were strongly associated with low probability of positive response to anti-CD20 mAbs therapy (figure 1).  Indeed, in the combined 4 clinical studies, 28% of biomarker-negative patients responded to treatment while only 9% of biomarker positive patients responded under the same conditions (Odd ratio: 3.6; 95% confidence interval: 1.8 to 8.4).  Of note, this combination biomarker does not appear to be an indicator of more severe diseases since it was not associated with different response rate in the placebo groups from those clinical studies.

Fig. 1

Beyond representing a major advance in the area of treatment decision in RA patients, this work represents a remarkable example of the power of well-planned, well-executed prospective retrospective studies for the discovery and clinical qualification of novel biomarkers

Thierry Sornasse for Integrated Biomarker Strategy

New Biomarker to Guide Antibiotic Prescription Decisions: Procalcitonin as Barometer of Infection

In the early online issue of September 22^nd of BMC Medicine (link; provisional paper), Philip Schuetz, Werner Albrich, and Beat Mueller review the present and the future promises of procalcitonin (PCT) as a potential generalized biomarker of infection and potential guide to antibiotic prescription in clinical settings.  As the authors point out, the field currently lacks reliable biomarkers of bacterial infection that can be assessed rapidly from easily accessible samples, resulting in suboptimal management of antibiotics administration.  Therefore, beyond the direct benefit of expediting the diagnosis of bacterial infection, PCT could be used to develop an antibiotic prescription algorithm that would potentially optimize antibiotics usage by eliminating their use in circumstances where they are not needed (fig. 1)

While strong evidences from randomized clinical trials support the use of PCT to guide the prescription of antibiotics for the treatment of lower respiratory tract infections (upper respiratory tract infection, pneumonia, COPD exacerbation, and acute bronchitis), and severe sepsis, more work needs to be done to establish PCT as a clinically relevant tool in the management of infections such as bacteremia, abdominal infection, neutropenia, and postoperative fever.

Thierry Sornasse for Integrated Biomarker Strategy

Catching Metabolic Pathways in the Act: Navigating the Heavy Water World

A press release on September 16^th on Market Watch about KineMed caught my attention (link).  KineMed, based in Emeryville CA, has developed new proteomics and metabolomics tools that enable the monitoring of metabolic flux through complex biological pathways by exploiting the power of deuterated water (or heavy water: ²H₂O) labeling.  By monitoring the kinetic of predictable mass shift of molecules of interest by mass spectrometry, the scientists at KineMed have been able to ascertain complex dynamic processes such as blood clotting, complement cascade activation, epidermal turnover in psoriasis patients, anterograde neuronal transport in ALS and PD patients, and DNA turnover rate in leukemia and breast cancer (see a video presentation by Marc K. Hellerstein, M.D., Ph.D.; co-founder of KineMed)

Because of its non-radioactive nature and ease of deployment (deuterated water is simply administered as a glass of water), this technique offers the prospect of identifying new biomarkers related to disease processes, drug mechanism of action, and drug toxicities.  It is important to remember though that this technique does not allow for in situ metabolism monitoring and thus still requires sample collection.  Therefore, the usual limitations associated with the collection of biosamples do apply to this new technique.

Thierry Sornasse for Integrated Biomarker Strategy

From Biomarker to Companion Diagnostic: of Analytical and Clinical Validation, Regulatory Affairs, and Intellectual Property

In the August 24^th early online issue of Drug Discovery Today (reference), Michael Nohaile from Novartis Pharma AG discusses the key factors required to translate a promising biomarkers into an effective companion diagnostic (CDx).  Based on a pragmatic staging scheme of drug – CDx co-development (figure 1), the author dissects the complex cross-functional interactions between of analytical and clinical validation, regulatory affairs, and intellectual property management.

Fig.1

On the analytical validation front, the author stresses the importance of timely assay platform selection, the need for proper consideration of pre-analytical parameters (see my earlier post: Biomarker Research: The Pre-analytical Puzzle), and the critical issue of the synchronization of the assay validation process to meet clinical development milestones.  Failure to complete assay validation before the initiation of pivotal clinical will require the conduct of complex and expensive bridging studies to satisfy the regulatory requirement for CDx.

On the clinical validation front, the author discusses the issue of adequate sample ascertainment rate from clinical studies in the context of prospective-retrospective (predefined analysis of samples from a completed study) CDx clinical validation strategies, and the issue of the statistical power for purely prospective CDx clinical validation studies.  In particular, serious consideration should be given to the decision of including or excluding marker negative patients in such studies.  On the one hand, inclusion of marker-negative patients is required to determine the positive and negative predictive value of the candidate CDx.  On the other hand, beyond being less expensive and potentially faster, studies that exclude marker-negative patients may also present an ethical advantage in cases where the potential treatment benefit is expected to be negligible in marker-negative individuals.

From a regulatory affairs perspective, the fact that CDx are regulated by the Center for Devices and Radiological Health (CDRH) implies that specific regulatory expertise is required for the successful prosecution of CDx (see also my earlier post about recent FDA guidance: Companion In Vitro Diagnostics (IVD) Development: some clarity at last).  In particular, the fact that the risk / benefit analysis for CDx is entirely tied to the risk / benefit profile of the associated drug implies a close collaboration between the drug reviewing authorities (CDER/ CBER) and the device reviewing authorities (CDRH).

Finally, from an intellectual property, the author discusses the issue of the timing of patent filing and the more global issue of patentability of biomarkers.

Thierry Sornasse for Integrated Biomarker Strategy

System Biology-Based Prognostic Biomarkers of Clinical Complications in Acutely Injured Patients

In the September 13^th issue of PLoS One (link), John D. Storey and colleagues report on inflammation-related gene expression signatures associated with differential clinical outcome in acute trauma patients.  Specifically, the authors analyzed the expression of inflammation-related genes in 168 blunt-force trauma patients over a 28-day period.  The genes and gene pathways that clustered differently between patients’ clinical outcome subgroups (based on Marshall multiple organ failure clinical score) were assembled into predictive modules of clinical outcomes.  Of particularly interest, the down-regulation of MHC II expression within 48 hours of trauma and up-regulation of p38-MAPK within 100 hours of trauma were particularly robust independent predictors of negative clinical outcome in this patient sample.

Considering that up to 60% of late trauma mortality is caused by infections, sepsis, and multiple organ failure multiple organ, the management of these inflammation-related complications remains a major unmet medical need.  In particular, the ability to predict the individual patient clinical trajectory early during trauma treatment remains a significant challenge for the medical community.  Therefore, the prospect of using gene expression as a prognostic biomarker to manage the care of trauma patients is of particular significance.

Thierry Sornasse for Integrated Biomarker Strategy

Brain Imaging Biomarker of Pain: I see how you feel

Our perception of biomarkers tends to be limited to the realm of measures that provide information about disease and drug activity.  In fact, biomarkers can provide a means to assess additional biological processes relevant to patient well being such as anxiety and pain.  In paper published in the September 13^th issue of PLoS One (link), a team of the Department of Anesthesia, Stanford University describes a new functional MRI-based (fMRI) biomarker for the identification of pain.  Because the sensation of pain can be subjective and can occur in the absence of detectable injury, the standard for assessing pain is based on patient self report.  While this traditional measure is readily assessable, it does not differentiate between the sensory and the psychological components of pain perception.  In addition, patient self reported pain assessment is impossible in individuals who are not able to communicate.  Therefore, development of an objective biomarker of pain is of great interest to the medical community. 

The team at Stanford performed a pilot study involving 24 individuals who were monitored by fMRI while being subjected to painful and non-painful thermal stimuli.  Using the results from the first 8 volunteers, the team used Support Vector Machine learning to develop a predictive model that then validated on the remaining 16 individual volunteers.  In this setting, the model accurately identified the type of stimulus with 81 % accuracy.

While the size of this study is not sufficient to draw definitive conclusions, it is tempting to speculate that the future of pain management in patients who are unable to communicate may improve dramatically

Thierry Sornasse for Integrated Biomarker Strategy

Prognosis of conversion from MCI to AD: of verbal memory, brain volume, and CSF biomarkers

In the September 2011 issue of the Archives of General Psychiatry (reference), Dr. Goldberg and colleagues report the results of the first study that examined the respective predictive values of cognitive measures, brain imaging, and cerebrospinal fluid (CSF) biomarkers in determining the risk of conversion from Mild Cognitive Impairment (MCI) to Alzheimer’s disease (AD).

In contrast with the multiple recent publications derived from the Alzheimer’s Disease Neuroimaging Initiative about biomarkers in AD (ADNI; see earlier post), this work identified measures of delayed verbal memory (Logical Memory delayed recall and Auditory Verbal Learning Test delayed recall) as the most reliable predictors of progression from MCI to AD.  While brain volume assessed by MRI (Left middle temporal lobe thickness) was identified as an additional predictive factor, the levels of Ab42 and Tau in the CSF did not add significant predictive value to their model (systematic stepwise logistic regression).

In commentary provided to Medscape (link), the lead author urged caution in interpreting this finding by stating that “Biomarkers unarguably work. However, cognitive markers, which are less expensive and less invasive, also work and provide strong complementary information”.

In my mind, the question is not so much whether cognitive assessment tools work better than CSF biomarkers but more about the applicability of these findings to the general practice of medicine.  Indeed, while CSF biomarkers are objective measures, the results of even the best cognitive tests are partially subjective: the skills of the person administering the test can have an influence on the results.  Therefore, one can wonder if, in the hands of the average neurologist or neuropsychiatrist, the verbal memory testing would perform as well and would outperform the objective measure provided by CSF biomarkers.

Thierry Sornasse for Integrated Biomarker Strategy

FDA Pharmacogenomic Biomarkers in Drug Labels

The list of pharmacogenomic biomarkers included the labels of FDA approved drugs has grown substantially over the last 10 years.  The most recent update from the FDA (Table of Pharmacogenomic Biomarkers in Drug Labels; 08/25/2011) lists 109 pharmacogenomic biomarkers included in the labels of 97 drugs (the labels of some drugs such as Imatinib and Warfarin include more than one pharmacogenomic biomarkers).

From a regulatory perspective, these biomarkers can be included in different sections of the drug labels (e.g. box warning, contraindication, clinical pharmacology), informing the prescribing physicians and the patients about identification of responders / non-responders, avoiding adverse events, and optimizing drug dosage.  The label information about pharmacogenomic biomarker can describe:

• Drug exposure and clinical response variability
• Risk for adverse events
• Genotype-specific dosing
• Mechanisms of drug action
• Polymorphic drug target and disposition genes

Functionally, the majority of the pharmacogenomic biomarkers currently included in the label of approved drug fall into the category of safety and efficacy markers related to drug exposure due to altered drug metabolism.  Indeed, 60 of the 109 pharmacogenomic biomarkers belong to the liver cytochrome P450 enzymes (CYP) which play a critical role in drug metabolism.  Other functional variants of enzymes involved in drug metabolism such as dihydropyrimidine dehydrogenase (DPD) and thiopurine S-methyltransferase (TPMT) also fall into this category. 

Although still representing a minority of cases, the number of drug efficacy pharmacogenomic biomarkers included in cancer drug labels has been growing (i.e. response biomarkers, predictive biomarkers).  In general, these biomarkers are designed to assist in the prescription decision by testing for the presence of the drug target. 

Examples:

• Imatinib: C-kit, BCR-Abl, PDGFR
• Trastuzumab: Her2/neu
• Vemurafenib: BRAF
• Tositumomab: CD20

As the field of biomarker development in support of drug development evolves, it is expected that this list of pharmacogenomic biomarkers included in drug labels will grow substantially, making the promise of personalized medicine a reality.

Thierry Sornasse for Integrated Biomarker Strategy

Diagnostic On-the-Go: Cell phone, microchip, ELISA, and ovarian cancer

In the September 1^st issue of Lab on a Chip, Wang and colleagues report a proof-of-concept for an easily deployable, point-of-care diagnostic system for the detection of the ovarian cancer HE4 biomarker (reference).  The team combined a simple microchip-based ELISA platform with the imaging capability of modern portable phones.  Interestingly, the performances (sensitivity and specificity) of the portable phone camera appeared to be superior to a stand-alone CCD camera.

Although this work may seem anecdotal at first glance, it constitutes a valuable step towards increased diagnostic accessibility through the translation of a standard “high-tech” laboratory method to a “low-tech” broadly deployable platform.

Thierry Sornasse for Integrated Biomarker Strategy

Low Cost Blood Protein Detection System: Of Aptamers, Gold, and Resonance

In the September 1^st issue of Biomedical Optics Express (reference), Zheng and colleagues present a proof of concept study for a novel type of biosensor for the detection of proteins in blood.  Briefly, the team immobilized amine-terminated aptamers – artificial oligonucleotides engineered to bind specific ligands – onto a gold modified surface and used Surface Plasmon Resonance (SPR) to detect the binding of the ligand; in this case thrombin.  This prototype sensor showed good performances (sensitivity, linearity, and reversibility) for the intended ligand (thrombin), in the presence or absence of high levels (400 nM) of BSA, suggesting that this technology could be applied to direct detection of reasonably abundant factors in blood.

Considering the relative inexpensive nature of the manufacturing process of this new biosensor and the relative simplicity of SPR detection, it is tempting to speculate that this technology could solve the issue of cost for current and new blood diagnostics.  Time will tell if the reported performance of this prototype biosensor will be reproduced for other blood proteins.

Thierry Sornasse for Integrated Biomarker Strategy

Pairing GWAS with in-depth metabolomics: assigning functions to genetic variants

In the September 1^st issue of Nature (reference), scientists from the Helmholtz Zentrum Munchen Institute in Munich, Germany, the Wellcome Trust/Sanger Centre, King’s College, and Metabolon, Inc. present the most comprehensive Genome Wide Association Study (GWAS) aimed at identifying relationship between individual genetic variations and specific metabolic pathways.  Using ultra-high performance LC-MS and GC-MS, the levels of over 250 metabolites, representing over 60 metabolic pathways, were analyzed in serum samples from volunteers enrolled in the German KORA F4 study (n= 1768) and in the British TwinsUK study (n= 1052).  From these measures, over 37,000 metabolic traits (concentrations or ratios of metabolite pairs) were derived and their association with about 600,000 SNPs was assessed.  The team identified 37 independent genetic loci with genome-wide significant associations with metabolic traits, 23 of which represented novel associations.  Moreover, among these 37 genetic loci, 15 overlapped with known disease-associated genetic loci, shedding new light on possible new pathobiological mechanisms of diseases such as diabetes, kidney failure, venous thromboembolism, and coronary artery disease. 

This remarkable work represents a major evolution in the field of GWAS by providing a means to place genetic information within a functional biological context.  Indeed, despite identifying thousands of disease risk loci, most GWAS are cataloging exercises offering little or no information about the biological processes potentially associated with the identified genetic variants.  

Thierry Sornasse for Integrated Biomarker Strategy