Download - Impact of biomarkers on clinical trial risk

1645

Review

ISSN 1462-241610.2217/PGS.13.167 © 2013 Future Medicine Ltd Pharmacogenomics (2013) 14(13), 1645–1658

Impact of biomarkers on clinical trial risk

Despite major scientif ic and technological advances in recent decades, the current state of drug development faces serious challenges [1]. This is colloquially referred to as the ‘innova-tion crisis’ and was formally recognized in 2004 when the US FDA launched the Critical Path Initiative [2]. This initiative was the FDA’s response to the innovation crisis and their “strat-egy to drive innovation in the scientific processes through which medical products are developed, evaluated, and manufactured” [101]. CEOs, industry leaders and academics from across the pharmaceutical world also frequently refer to this decline in productivity as one of the great-est obstacles facing the industry [3–5]. This crisis can be best illustrated by the well-documented and divergent trends in drug development costs and drug approval rates: over recent decades, the costs of research and development continued to rise [6–8]; meanwhile, over the same period, the number of new molecular entities and FDA drug approvals continued to decline [1,9].

This recent strain on the pharmaceuti-cal industry has brought a new focus on drug development failures and potential strategies to mitigate this growing risk. One such strategy is to leverage pharmacogenomics and to actively begin incorporating biomarkers into clinical development [2,10]. The innovation problem can be described in many ways; here, we view this problem through the lens of risk – the likelihood that an experimental drug will be terminated due to concerns with efficacy, safety or com-mercial viability. In particular, we are concerned with clinical trial risk – experimental design

choices that may put a drug at a greater risk of failure. In this paper, we provide a review of bio-markers in the clinical setting, highlight what is currently known about them and discuss their potential impact on clinical trial risk.

BiomarkersThe advent of a working draft of the human genome in 2000 [11,12] and the complete genome that followed in 2003 [13] has led to many scien-tific insights and advances. One such advance-ment has come in the form of pharmacogenom-ics – the study of genetic differences in patients and how these influence the variability in drug response [14]. The idea of personalized medicine, whereby a treatment can be customized by a patient’s unique clinical, genetic, genomic and environmental information [15], was bolstered by the emergence of pharmacogenomics and is viewed as a potential answer to the innovation crisis. Specifically, the importance of biomarkers was stressed by the FDA in a report that fol-lowed the Critical Path Initiative in 2006: the Critical Path Opportunities report [102]. This report described and outlined the specific areas where the “sciences of product development had the greatest need for improvement.” Biomarker development was ranked as the highest priority.

Biomarker definitionsIn 2001, the Biomarkers Definitions Working Group defined a biomarker as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, patho-genic processes, or pharmacologic responses to a

The last decade has witnessed the cost of drug development rise dramatically; concurrently, the number of new drug approvals has declined. Clinical trial failure rates have contributed significantly to this ‘innovation’ crisis and are directly related to clinical trial risk. One strategy that is often touted to resolve this challenge depends on embracing a personalized medicine approach where treatment is tailored to a patient’s unique genetic background. We highlight a new risk-based paradigm of clinical trial risk that evaluates the utility of biomarkers in drug development and their risk mitigation benefits. Furthermore, examples elucidating the current state of biomarker integration during clinical trials and the potential risks posed by doing so will be discussed.

KEYWORDS: biomarker n clinical trial n cost n drug development n FDA n innovation n risk

Geoffrey Gilbert John Reid1,2, Tarek Abdullah Bin Yameen1 & Jayson Lee Parker*1

1Department of Biology, University of Toronto Mississauga, Mississauga, ON, Canada 2GlaxoSmithKline Canada Inc., Mississauga, ON, Canada*Author for correspondence: Tel.: +1 289 337 [email protected]

part of

For reprint orders, please contact: [email protected]

Pharmacogenomics (2013) 14(13)1646 future science group

Review Reid, Bin Yameen & Parker

therapeutic intervention” [16]. This is now a con-sensus definition in the literature and has been widely adopted therein. From an FDA perspec-tive, a genomic biomarker has been defined as: “A measurable DNA and/or RNA characteristic that is an indicator of normal biologic processes, pathogenic processes, and/or response to thera-peutic or other interventions” [103]. Finally, the term has been subsequently expanded to account for different types of biomarkers, including phar-macodynamic, predictive, prognostic and surro-gate biomarkers. These biomarker subsets are broadly used and defined in other works [17,18].

Another source of confusion with biomarker nomenclature appears to come from the terms ‘validation’ and ‘qualification’. Although inter-twined, the terms ‘biomarker validation’ and ‘biomarker qualification’ are distinct [19]. The term ‘biomarker qualification’ is reserved for the evidentiary process that has biological pro-cesses and clinical end points linked with a bio-marker. Conversely, ‘validation’ in the context of a biomarker refers to the assessment of an assay or measurement performance characteristic, including sensitivity, specificity and reproduc-ibility. Simply put, validation is associated with an analytical method, while qualification refers to the linking of a biomarker with a clinical end point [19,20]. The confusion with biomarkers also seems to extend to the regulatory bodies and the biomarker assay approval processes that have been established to date. Most recently, it appears that regulatory bodies are shying away from the term ‘validated’ biomarkers [5], instead qualifying biomarkers for a particular use [2]. As the FDA process has immediate practical impact on drug development, we assign priority to their views on these matters.

A risk-based paradigm of clinical trial designAs defined previously, drug development risk is multifaceted and has various sources (i.e., pre-clinical, clinical and commercial). The largest of these sources is clinical trial risk, given the attrition rates experienced by drugs during clini-cal testing [21–23]. It is widely recognized that clinical trial failures, resulting from a variety of causes [8], are a major contributing factor to the recent trends observed in research and develop-ment costs [1,5]. Clinical trial failure rates are significant and provide a sobering picture of the reality of drug developmental risk [3–5]. Thus, in order to reduce cost, increase productivity and overcome the innovation crisis, the issue of clini-cal trial risk must be addressed.

A risk-based paradigm of clinical trial design examines the transition rates of a drug for a spe-cific indication. For example, it was reported that HER2-positive patients with advanced or metastatic breast cancer had a pass rate of 29% in Phase II, meaning that of the drugs that have completed Phase II testing, 71% failed to move onto Phase III testing [24]. This analysis looks at the ratio of drugs that move onto further test-ing compared with the total number of drugs that completed testing regardless of whether they advanced to Phase II. A drug that is still under-going Phase II testing is not classified in this example, as we do not know the outcome of the Phase II trial. Drugs that prematurely terminate clinical trial testing due to safety concerns or a lack of efficacy are also included, and would be failure outcomes. In summary, a risk-based para-digm provides a transparent and unambiguous way to assess the success rates of drug testing.

Our view of this crisis is to look at the chal-lenges of drug development through the lens of risk. In a risk-based paradigm of drug develop-ment, we use historical data to determine the risk of trial failure based on the disease area, choice of drug, choice of clinical end point and choice of drug target. For example, drug failure rate analyses have been conducted relative to: drug class and target in HIV-1 [25], rheumatoid arthritis [26], breast cancer [24] and Crohn’s dis-ease [27]; biomarker use in melanoma [Rubinger

D, Hollmann S, Serdetchnaia V, Parker JL. Biomarkers

reduce clinical trial failure risk in metastatic mela-

noma (2013), Manuscript in preparation]; mechanism of action in prostate cancer [28]; biomarker use and mechanism of action in lung cancer [29]; and finally, rates have also been described relative to clinical end point in non-Hodgkin’s lym-phoma [30]. In this paradigm, we forecast drug risk without basing it on our understanding of either the drug’s mechanism of action or the strength of the Phase II data. Data in almost all disease areas indicate poor performance, with the typical ‘industry’ success rates of bringing a new drug to market from Phase I clinical stud-ies at approximately 25% [21]. This suggests that this approach has not availed us of this problem. Privately, frustration is felt among healthcare professionals and the pharmaceutical industry as failures persist. In a risk-based paradigm of clinical trial design, we quantify risk based on the historical performance of the disease area. For example, in HIV, depending upon the choice of target in the replication pathway of the virus, clinical trial risk can range from 7 to 25% [25]. Such risk estimates are based on an objective

www.futuremedicine.com 1647future science group

Impact of biomarkers on clinical trial risk Review

application of historical data for each target in question. A risk-based paradigm of clinical trial design allows a fresh assessment of the utility of biomarkers in drug development and their risk mitigation benefits.

Clinical trial risk is also a matter of perspec-tive. For some physicians, the concern is that we will miss the next big drug that will bring a new level of treatment benefit to patients. From an investment and financial perspective, it is the red herrings or false positives that are caus-ing financial chaos, as promising compounds advance from Phase II into expensive Phase III trials, only to fail during such pivotal testing. A relatively recent example, iniparib, demonstrated great results in Phase II only to fail to meet the primary end point in Phase III [31]. Failed trials also carry different forms of opportunity costs. This is twofold, where patients are exposed to drugs that turn out to be ineffective or danger-ous, and physicians commit time that could be better spent elsewhere. There are implications for funding agencies as well. For example, our recent estimate of clinical trial risk for castration-resistant prostate cancer is 97% [28]. This means that of all the drugs that enter Phase I testing, a mere 3% will achieve FDA approval and become available to patients. A funding agency may decide that other cancers, with much lower clini-cal trial risk but still representing a significant burden to society, should be funding targets. It is not our position as authors to advocate priori-ties, but it is incumbent upon us to provide the best data to support such decisions. A risk-based paradigm of clinical trial design is not just about a more transparent estimate of risk, but a form of decision support so that physicians, patients and funding agencies can better gauge the best use of their time and resources.

Biomarkers to the rescue?It has been stated that the use of biomarkers in the clinical setting can reduce clinical trial failure rates and thus reduce risk in drug development [10,32]. It has also been acknowledged that bio-markers may have the potential to increase the safety and effectiveness of new drug treatments [2,103]. However, the impact of biomarkers on clinical trial risk, and thus the innovation crisis, has not been empirically examined until recently.

The risk is clear when the biomarker qualifi-cation process is completed, but to achieve this is a significant challenge for a company. This is especially true when companies work in isolation, which is a consequence of working in an industry where proprietary information is at a premium.

In the context of the FDA, there are three types of biomarkers based on validity: exploratory, prob-able valid and known valid [17,104]. A known valid biomarker is defined by the FDA as: “A biomarker that is measured in an analytical test system with well-established performance characteristics and for which there is widespread agreement in the medical or scientific community regarding the physiologic, toxicological, pharmacologic, or clinical significance of the results” [104]. A prob-able valid biomarker, in this context, may not be a valid biomarker because it is lacking conclusive data or has not been independently replicated. The qualification process, laid out in further FDA guidance [103], is designed to bridge the gap from exploratory to known valid biomarkers [2,19,33]. Finally, within this qualification process is the identification and validation of the appropriate assay [19]. Thus, from a clinical trial risk perspec-tive, the codevelopment of a new biomarker along with a new drug would, in theory, increase clini-cal trial risk (Figure 1). Prima facie, the last thing the drug development process needs is another source of risk. From our perspective, we would go further than the FDA’s definition of a known biomarker and would not consider a biomarker to be validated until it had been part of a new drug approval in the past.

Over the last two decades, many studies have documented and quantified clinical trial failure rates to estimate risk [21,34–40]; however, very few have examined the implications, if any, of biomarkers on these rates. Two notable excep-tions, and we believe these to be the only two, are papers published in 2012 by Parker et al. [24] and in 2013 by Hayashi et al. [41]. Hayashi et al. described the clinical trial success rates and transitional probabilities of 908 antitumor agents relative to the use of a biomarker in the clinical trials. Their results showed that when a biomarker was included in the clinical studies, the transitional probabilities were higher when

Clinical trial risk

New drug New biomarker

• Safety• Efficacy

Quali�cation:• Predictive• From exploratory to valid• Independently replicated

Figure 1. Sources of clinical trial risk when a new biomarker is codeveloped alongside a new drug.



compared with clinical studies carried out in the absence of a biomarker; this was true for all phases. In addition, the use of biomarkers being relevant to other indications was also suggested in this study. However, as so many indications were covered in one study, it is not quite clear how each indication was investigated.

Parker et al. was a retrospective observational study that examined the clinical trial success rates in breast cancer relative to the presence of the well-established HER2 biomarker [24]. The major conclusion drawn from this study was one of differential and biomarker-dependent clinical trial success rates: clinical trial success rates of drug candidates in patients identified with the HER2 biomarker (23%) were greater when com-pared with drug candidates in anthracycline/taxane-exposed patients (15%). This suggested an approximately 50% reduction in clinical trial risk in this indication. However, there were con-cerns that such findings were specific to such a well-established biomarker as HER2, or that they were specific to the type of breast cancer population studied. Unpublished studies in non-small-cell lung cancer (NSCLC) [29] and melanoma [Rubinger D, Hollmann S, Serdetchnaia V,

Parker JL. Biomarkers reduce clinical trial failure risk

in metastatic melanoma (2013), Manuscript in prepara-

tion] have extended this finding to other indica-tions that are dominated by novel biomarkers that were never approved by the FDA. In our metastatic melanoma study, of the 11 biomarkers used, only two had a prior history of approval with the FDA and were thus not novel (c-kit and HER2). Furthermore, in our study on advanced metastatic NSCLC, of 11 biomarkers, only one was not novel (ALK). These findings are also consistent with a broad review conducted by Hayashi et al. [41]. Collectively, this suggests that in risk-based paradigms of clinical trial design, biomarkers have a very quantifiable impact.

Barriers to the adoption of biomarkers by industryFrom the standpoint of scientists, the utility of biomarkers is not surprising, but this view is not uniformly shared by all stakeholders in indus-try. In our experience, personalized medicine has assumed the mantle of political correct-ness, and along with it, biomarkers have been cast in the same light. Confidential discussions with CEOs of biotechnology start-up companies have revealed a reluctance to pursue biomarkers. Opposition to biomarkers is based on the per-ceived effect biomarker adoption has in reduc-ing the target market size and thus future sales

revenue of a drug. In other words, the perceived benefits of biomarkers by some stakeholders in industry is not worth the impact of a smaller market size for a given therapeutic. As the inno-vation crisis continues to worsen, biomarkers have received much attention and increased adoption [42]. To date, it appears that crisis is a key driver in the pursuit of biomarkers. Finding a biomarker to increase efficacy or decrease safety events can be key to FDA approval or avoiding market withdrawal. For example, natalizumab has demonstrated strong benefits in patients with multiple sclerosis, but there continues to be concerns regarding the frequency of a rare but serious side effect called progressive multifo-cal leukoencephalopathy [43]. The manufacturer appears to be placing greater emphasis on finding a biomarker or a pattern in patient history to predict the risk of progressive multifocal leuko-encephalopathy among multiple sclerosis patients treated with natalizumab [38]. Biomarkers are embraced by industry if they can be seen as tools to avoid imminent crises. In our experience, those stakeholders in industry that have reserva-tions concerning the business case for biomarkers will not voice such skepticism publicly due to the mantle of political correctness that surrounds personalized medicine and biomarkers. There is strong public support for personalized medicine [44] and thus it is poor branding for any firm to be open about their reservations on this initiative. Exploring the impact of biomarkers on clinical trial risk is important both scientifically and in order to address silent critics in industry.

Biomarkers in the context of FDA labelingIn order to better understand the current state of biomarker incorporation into clinical trials and its impact on clinical trial risk, it is nec-essary to consider the drugs that are already approved with biomarkers. A list of qualified genomic biomarkers identified in the context of approved drug labels was accessed from the FDA ‘Table of Pharmacogenomic Biomarkers’ website [105]. This comprehensive list highlighted biomarkers including, but not limited to, chro-mosomal abnormalities, expression changes, functional deficiencies and gene variants. Note that microbial variants that influence response to anti-infectives were not included in the list. On the date of access, 8 January 2013, 105 drugs and 38 biomarkers were represented in the table produced by the FDA. In some cases (e.g., imatinib), there are multiple indications for a single drug that include a biomarker in the



prescribing information. As a result, the table from the FDA includes 118 unique drug–bio-marker combinations. For simplicity, we will refer to these 118 combinations as individual drugs, even though, as mentioned, a single drug may appear in the list multiple times. Using this list, the phase of biomarker incorporation was elucidated and categorized (Figure 2). Through our research, six drugs remained classified as undetermined. Some reasons for this include a biomarker that was also the indicated disease for a given drug (sodium phenylbutyrate for the treatment of urea cycle disorders) and a bio-marker that was included in prescribing infor-mation on theoretical grounds (mycophenolic acid with the biomarker hypoxanthine-guanine phosphoribosyltransferase).

Based on our categorization, 29 drugs that were approved by the FDA had a biomarker incorporated sometime from Phase I onwards. Seven of these drugs had a biomarker incor-porated sometime during human clinical tri-als (Phase I–III). Highlighted in this review are these seven drugs and their respective bio-markers. In addition, key examples will be examined of the 22 remaining drugs that have had a biomarker incorporated at Phase IV. We highlighted these biomarkers in order to better understand how and why biomarkers are incor-porated later in the development pathway (i.e., sometime after preclinical studies). This is under the assumption that drug developers, generally speaking, are hesitant to integrate biomarkers into later phases as it introduces additional risks. A failure at this point could amount to hundreds of millions of US dollars in losses. By contrast, incorporated biomarkers during preclinical stud-ies do not pose as much risk, since drug failures at this point would amount to inconsequential costs. Drug developers’ willingness to incorpo-rate biomarkers during preclinical development is apparent when considering that 27 of the 118 drugs in the list provided by the FDA had a biomarker incorporated at the preclinical stage. This number is even greater when you consider the cytochrome P450 (CYP) superfamily of drug metabolizers. Many of these biomarkers are used during screening and in preclinical studies, and this has become common practice [45].

Commercial risks of biomarker integration during clinical Phases I–IVWhen a drug is initially developed in the preclini-cal phase, researchers attempt to demonstrate effi-cacy in the broadest patient population possible

in order to maximize its commercial success. However, when a biomarker is introduced in the later phases (i.e., Phase I–III), this creates a new source of unmitigated risks, which can reduce the market viability of the drug [46]. First, a bio-marker can substantially constrict the intended patient population, which in turns reduces the potential revenue generated. Second, develop-ment costs can increase from the need to validate a clinical biomarker for regulatory approval as a diagnostic test. Third, the high exclusion rate due to biomarker stratification can dramatically reduce patient recruitment, which can require more investigator sites in order to compensate for it. Thereby, this can increase the development costs of the drug and the duration of the studies required [47]. Therefore, the industry must bal-ance the initial, albeit risky, investment in a bio-marker against the longer-term presumed benefits of increased efficiencies in drug development and return on investment [46]. To fully assess this risk–benefit profile of biomarker incorporation during clinical trials, market size reduction following clinical integration was determined in order to conceptualize the commercial risks involved with a reduced patient population (Figure 3). Market size reduction was calculated by determining the biomarker prevalence in either the disease (ALK [48], KRAS [49] and PML/RARα [50]) or the general population (chromosome 5q [51], UGT1A1 [106] and IL28B [52]). As is evident, biomarker inte-gration in clinical trials is often associated with

Preclinical

Phase I

Phase II†

Phase III‡

Phase IV

CYP family

Undetermined

27

23

2

22

56

6

Figure 2. Biomarkers by phase of incorporation into the drug development pathway. The drugs depicted were approved by the US FDA with pharmacogenomic biomarkers. A total of a 118 unique drug–biomarker combinations were examined. †Three biomarkers was incorporated during what would typically be called a Phase I/II trial, but is classified here as Phase II for simplicity. ‡Incorporated by way of a retrospective analysis of Phase III data. Analysis coincided with FDA approval (biomarker was incorporated premarket).



significant market size reductions, ranging from 2 to 93%. However, in most cases, this market size reduction was typically well above a 40% reduction threshold. Thus, it appears that the integration of biomarkers during clinical devel-opment introduces an additional source of risk: reduced sales revenues.

Proponents of a biomarker-driven clinical development model counter that a restricted patient population ensures the commercial viability of the drug [47]. Personalized medicine hinges on enriching a patient population in clin-ical trials based on biomarker status, thereby, in theory, demonstrating efficacy earlier [31]. Alter-natively, biomarker use could improve the safety profile by identifying patients who are at risk for serious adverse events. Therefore, based on its clinical superiority in a given population, these drugs will enjoy faster and wider adoption rates among patients and their physicians.

In addition, it has been argued that a bio-marker-driven approach could accelerate the drug development process by increasing the success of clinical trial outcomes [22,31]. Bio-markers would act as surrogate end points in clinical development. The reduced trial sizes required to detect efficacy, and shortened trial duration based on surrogate end points, could substantially reduce development costs. Con-sequently, these abridged development periods would extend the revenue generation based on a longer patent life. Hence, it is thought that

the use of a qualified biomarker can robustly improve the success of a clinical program. How-ever, the incorporation of a ‘novel’ biomarker into a clinical program requires a multifaceted approach to risk mitigation, as the biomarker validity and therapeutic efficacy of the drug need to be evaluated simultaneously. Taken together, risk factors involving the concurrent validation of a biomarker and the therapeutic efficacy of a drug would suggest greater clinical trial risk for the development of the drug. Fortunately, the research to date suggests that this potential for added risk is unfounded [24,29,42] [Rubinger D, Hol-

lmann S, Serdetchnaia V, Parker JL. Biomarkers reduce

clinical trial failure risk in metastatic melanoma

(2013), Manuscript in preparation].

The certolizumab pegol story: is there potential to reduce the treatment population unnecessarily?The clinical development and final approval of certolizumab for the treatment of Crohn’s disease provides a unique example of the use of a biomarker in clinical trials. Certolizumab pegol was approved by the FDA based on two pivotal Phase III studies – PRECiSE 1 [53] and PRECiSE 2 [54] – both of which were designed with the influence of a biomarker, CRP serum levels. CRP is a liver-derived acute-phase pro-tein and its serum levels have become a good definition of flare-up [55]. The PRECiSE trial designs were based on a prior Phase II study that suggested certolizumab pegol may have greater efficacy in patients with elevated levels of CRP. The potential predictive value of CRP was elu-cidated from the Phase II trial through post hoc analysis [56]. This result prompted the Phase III program to be designed with a primary end point relative to patients with CRP levels ≥10 mg/l. What is interesting here is that the FDA consid-ered both Phase III pivotal trials in their review, yet CRP, as a biomarker, never appears in the prescribing information. It would appear that the FDA conclusion was that the Phase III data were strong enough to suggest that the benefit of the drug should not be restricted to Crohn’s disease patients with high CRP. However, in our view, this raises a few concerns. This scenario provides a clear example of the potential for a novel biomarker to be inappropriately identified as clinically significant (in a Phase II study in this instance). In the case of certolizumab, it was then perhaps plausible for the Phase III program to have been designed to only include patients with elevated CRP, which, if carried out, would have unnecessarily reduced the treatment population.

0

10

20

30

40

50

60

70

80

90

100

ALK KRAS UGT1A1 PML/RARα IL28BChromosome5q

Biomarker

Mar

ket

size

red

uct

ion

(%

)

Figure 3. Market size reduction for drugs that incorporated biomarkers after preclinical studies for safety or efficacy outcomes. Market size reduction was calculated by determining the biomarker frequency in the primary therapeutic area of the drug in a North American population, based on US Census data when necessary.



Thus, from the perspective of the manufacturer, the market size would have been reduced. From the physician’s perspective, it could result in a label that restricted patients that would have benefited from the drug. Therefore, this example illustrates a case where the incorporation of a novel, unqualified biomarker added risks to the drug both clinically and commercially.

Chronicling biomarker integration into clinical developmentIn our view, in order to understand clinical trial risks, we need to not only examine a single trial, but also the earlier trials that have brought us to this point. Similarly, in order to understand the current use of biomarkers, we need to look at the entire clinical pathway. What are the cur-rent practices for the use of biomarkers? At what point are they incorporated into the clinical development path of a drug? In this section, a brief overview of the circumstances surround-ing biomarker incorporation during Phase I–III studies, and a few key postmarket examples, will be given in order to highlight the various factors behind this decision-making process (Figure 4).

One of the crucial decision-making factors behind the incorporation of biomarkers during clinical development is the discovery of increased efficacy in a given population. An example is crizotinib (Pfizer, NY, USA), which entered Phase I studies as a mesenchymal–epithelial transition (MET) multitargeted receptor tyrosine kinase inhibitor for the treatment of NSCLC [57]. The Phase I trial (A8081001, NCT00585195) was an open-label, multicenter, two-part study investigating the maximal tolerated dose and effi-cacy in a molecularly enriched cohort of patients harboring MET amplifications or MET muta-tions. Simultaneously with the initiation of this clinical trial, two independent groups concur-rently reported the discovery of ALK rearrange-ments in NSCLC [58,59]. ALK gene alterations play a pivotal role in the pathogenesis of anaplastic large-cell lymphomas, myofibroblastic tumors and neuroblastomas [58]. Thus, these discoveries highlighted the presence of ALK rearrangements for the first time in common solid tumors [57]. Therefore, this provided a novel biomarker as a potential therapeutic target for future treatments of NSCLC. However, the incorporation of the ALK biomarker into crizotinib’s clinical trials did not occur. At this point, this would have intro-duced unprecedented risk factors, since crizotinib was primarily developed for MET mutations, and its efficacy in ALK rearrangements were unknown to investigators.

Not surprisingly, when a drug exhibits sig-nificantly improved outcomes in a particu-lar biomarker-defined population, investiga-tors reactively incorporate the biomarker into clinical development. This was evident when two ALK-rearranged NSCLC patients exhib-ited dramatic improvements in symptoms; the protocol was amended to permit prospective screening of ALK-rearranged NSCLC [57]. Note that screening for patients with MET-amplified or -mutated tumors continued concurrently. In 2009, initial efficacy results in the first 19 ALK-rearranged NSCLC patients demonstrated an overall response rate (ORR) of 53% and a disease control rate, defined as ORR and stable disease, of 79% [60]. This impressive therapeu-tic efficacy was further validated as results from a growing cohort of ALK-rearranged NSCLC patients in the Phase I study exhibited substan-tially improved ORR and disease control rates [48,61]. Subsequently, when the Phase II study (NCT00932451) was conducted, the inclusion criteria changed to include only patients with an ALK fusion gene, as detected by a standardized break-apart FISH assay [62]. Therefore, the effi-cacy of crizotinib in ALK-rearranged NSCLC in the Phase I study and the lack of MET screening in the Phase II study indicate that, at this point, the investigators terminated crizotinib’s devel-opment as a MET inhibitor. Hence, this high-lights efficacy as a crucial factor in the decision- making process of biomarker integration. In particular, the decision to integrate the ALK biomarker was driven by increased efficacy in a biomarker-defined population. Consequently, by constricting the patient population, this inher-ently reduced clinical trial risk by maximizing the drug’s therapeutic success. In short, the inclusion of biomarkers accelerates the clinical development of a drug as it demonstrates efficacy earlier and more robustly. This finding is evident in crizotinib’s case as it took only 4 years from the discovery ALK-rearrangement in NSCLC to crizotinib’s conditional approval as a first-in-class ALK inhibitor treatment for NSCLC.

Another compelling factor behind biomarker integration is the identification of a biomarker that can decrease safety events. This reduces clinical trial failures associated with unacceptable risk–benefit profiles based on therapeutic efficacy and the incidence of adverse events. In addition, the ability to identify a biomarker in light of increasing safety events can prevent a potential market withdrawal by the regulatory bodies. As discussed previously, this is the current reality for natalizumab, but there are other examples where



this has also occurred. Irinotecan was approved as a first-line therapy for patients with metastatic colorectal cancer (mCRC) in combination with

5-fluorouracil and leucovorin [63]. Despite its effi-cacy in terms of prolonging survival, its usage was associated with severe diarrhea and neutropenia

Biomarker and drug developmental stage

Phase I Phase II Phase III US FDAapproval

Postmarketingstudy

Labelchange

ALK Pivotal trial 26 August 2011

28 December2005

12 February 2004

27 September2009

14 June 1996

19 October 2007

31 October 1991

22 November1995

19 July2009

19 July2009

7 June2005

25 September2000

Chromosome 5q

Pivotal trial

Pivotal trial KRAS-

Pivotal trial KRAS-

Pivotal trial UGT1A1

UGT1A1 Pivotal trial

Pivotal trial APOE2

PML/RARα

Pivotal trial

PML/RARα

Pivotal trial

IL28B 31 May2011

Pivotal trial IL28B

Pivotal trial IL28B

Drug

Crizotinib: non-small-cell lungcarcinoma

Lenalidomide:myelodysplasticsyndromes

Cetuximab:metastaticcolorectal cancer

Panitumumab:metastaticcolorectal cancer

Irinotecan: colonor rectum cancer

Nilotinib: chronicmyelogenousleukemia

Pravastatin: hypercholesterolemia

Tretinoin/all-transretinoic acid:acute promyelocyticcancer

Arsenic trioxide: acute promyelocyticleukemia

Peg-IFN-α-2b:hepatitis C

Boceprevir:hepatitis C genotype 1

Telaprevir:hepatitis C genotype 1

19 January 2001Pivotal trial

13 May2011

23 May2011

Figure 4. Key milestone timelines for 12 drugs that have had a biomarker incorporated during the clinical trial phases or subsequent to US FDA approval. Noted are the drugs’ indications, stage at which the specific biomarker was incorporated (pink), the phase of the pivotal trial (light blue), the date of US FDA approval (dark blue) and, in the case of a postmarket biomarker incorporation, the date that the prescribing information changed relative to the biomarker (green).



in 20–35% of cases [64]. In fact, these safety con-cerns were identified during Phase I studies, but their unpredictable onset caused clinical investi-gators to manage them, rather than identifying their root causes in the Phase II and III studies [65]. Despite significant concerns regarding its observed toxicity, irinotecan became the standard of care based on its apparent survival benefit from clinical trials [65]. However, uproar ensued when postmarketing studies investigating alternative dosing regimens reported treatment-related fatal events to be as high as 5.3%, up from a preap-proval rate of 1% [66,67]. The excessive rate of early deaths along with an inconsistent toxicity profile altered perceptions regarding irinotecan’s role in mCRC treatment [66]. Thus, the absence of a pre-defined biomarker that could accurately predict these intolerable fatal events posed a significant risk. The lack of a genotypic predictive factor presented an unacceptable risk–benefit profile for physicians despite the drug’s efficacy in terms of increasing survival in mCRC. Therefore, without a biomarker, the clinical utility of irinotecan as the standard of care was being challenged and revised.

In order to elucidate a genotypic predictive fac-tor for irinotecan’s toxicity profile, its metabolism was investigated. In particular, UGT1A1 variabil-ity was investigated as a candidate for predicting severe toxicities, as levels of SN-38, irinotecan’s active metabolite, were thought to be modulating these toxic effects [65,68]. Thus, subsequent pro-spective investigations concluded that patients that were homozygous for the UGT1A1*28 allele were at an increased risk of hematological toxic-ity and/or diarrhea [63,69,70]. Even though these prospective investigations established a highly correlated response, it required further valida-tion, as the results were inconsistent. Nonethe-less, based on these findings, the FDA Advisory Committee on Pharmaceutical Sciences recom-mended that the product information should be amended to include an association between UGT1A1*28 and hematologic toxicity [64]. As a result, Pfizer submitted a supplemental New Drug Application (#20-571/S-026) [107] request-ing the inclusion of the UGT1A1*28 polymor-phism in the ‘Warning’ section of the prescribing information [108]. This resulted in a label change that identified the homozygous UGT1A1*28 population to be at an increased risk for neutro-penia, and recommended a lower starting dose, despite the variability of the clinical studies to date [108]. Additional studies following this label change were inconsistent and failed to conclu-sively prove the UGT1A1 polymorphism to be

a predictive factor of irinotecan-induced safety events [71,72]. Eventually, a meta-analysis of nine trials identified a strong association between UGT1A1 polymorphism and neutropenia at high- and medium-dose regimens, but not at low doses [73]. In short, the inclusion of a bio-marker, despite a lack of uniformity in clinical trial results, provides a compelling narrative for its risk reduction benefits. By pre-emptively iden-tifying a patient subpopulation that is potentially at an increased risk of safety events, the perceived therapeutic efficacy of the drug was safeguarded. In particular, this biomarker integration insured the clinical utility of irinotecan by allowing for its continued use as the standard of care until a more efficacious and safer treatment, oxaliplatin, became evident [74,75]. Thus, this strategic use of biomarkers, even after regulatory approval, was able to reduce clinical trial risk by preventing a potential postmarketing withdrawal through identifying patients at increased risk of serious adverse events.

Implications of risk in the case of CYP superfamily drug metabolizersCYP superfamily enzymes are a diverse, distinct and well-characterized group of proteins. The predominant function and relevance here is their role in drug metabolism [76]. The importance of pharmacogenomics relative to CYP superfam-ily drug metabolizers was established 40 years ago. The presence of polymorphisms in the CYP superfamily means that different individu-als will respond uniquely, yet predictably, to a given drug [77]. For these reasons, we decided to examine them separately. Out of the 118 drugs approved by the FDA with pharmacogenomic biomarkers, 56 (47.5%) are approved with a CYP superfamily enzyme. Only four isoforms are rep-resented, with the vast majority of these being CYP2D6 (37). The other three are CYP1A2 (1), CYP2C19 (15) and CYP2C9 (3). From a FDA labeling perspective, these biomarkers can appear in a number of sections and have a variety of consequences, ranging from contraindication in thioridazine to a description of the interac-tion, or lack thereof, in prasugrel (Figure 5).

It has been widely established that the CYP superfamily is involved in the metabolism of up to approximately 90% of drug substrates [78–83]. Therefore, these enzymes have a considerable impact on the pharmacokinetics of most drugs, which translates into effects on absorption, distri-bution, metabolism and excretion. These are sig-nificant issues in drug development: it has been reported that a poor pharmacokinetic profile



accounts for over 50% of compound failures in drug discovery and development [84].

As a result of the documented impact of drug metabolizers, drug manufacturers have largely accepted the early incorporation of these bio-markers into their drug development pathways. The ubiquitous use of the CYP superfamily metabolizers in FDA prescribing information also speaks to this point. In fact, in our research, most FDA drug labels include a dedicated sec-tion to the interaction of a given drug and the CYP superfamily metabolizers. This includes many biomarkers that do not appear on the FDA pharmacogenomic table. Another concern is raised based on this fact, which is the incon-sistency in the FDA’s approach to documenting CYP superfamily enzymes in their drug labels.

Manufacturers appear to have embraced any risk of incorporating drug metabolizers into their drug development pathways. A main reason for this is that these drug metabolizers are already qualified and validated. This means that manu-facturers do not have to codevelop the biomarker with their drug. As a result, it follows that clinical trial risk should not be significantly increased. It has been established that early integration of CYP superfamily metabolizers in drug development has implications in terms of clinical trial risk [85].

By screening and predicting pharmacokinetic properties early, compounds that are destined to fail would be identified sooner. The result, ide-ally, is a reduced number of late-stage failures, which companies are doing everything to avoid, due to the now staggering investment required.

ConclusionDrug manufacturers continue to struggle with the development of new medicines. The FDA has recognized this challenge. The prospect of incor-porating biomarkers into clinical trials has been advocated as a strategy to improve the current situation that the industry is facing. However, to date, only two studies have empirically examined the impact of biomarkers on clinical trial failure rates. Validation also remains to be a key barrier in biomarker integration and from our perspec-tive, we would take the FDA’s definition a step further and would not consider a biomarker vali-dated until it appeared in the labelling of a FDA approved drug. More research is needed in order to fully understand how biomarkers impact drug development and what incorporation at different stages means to the overall success rates. We have outlined a brief overview of the current trends and practices in biomarker incorporation with respect to FDA labeling, and highlighted this with key examples. We view the crisis through the lens of risk and we argue for a risk-based paradigm of clinical trial design that would allow for a fresh assessment of the utility of biomarkers in drug development and their potential to mitigate risk.

Future perspective We envision a risk roadmap for clinical trial devel-opment, where every choice, including the choice of biomarker and the phase in which it is incor-porated, gives us a specific risk estimate of drug failure. With respect to clinical trial design, this goes even further, covering such considerations as end point selection, target selection and choice of drug technology, to name but a few. It may be that biomarkers reflect the underlying reality that, with the exception of single-gene defects, nearly all diseases are heterogeneous. Biomarkers may be the first step in challenging our current diagnosis of diseases, and they may allow us to further segment patient populations into more specific phenotypes. From an operational perspec-tive, this comes down to the standard questions of the inclusion and exclusion criteria for any clini-cal study. A risk-based paradigm of clinical trial design will allow us to bridge biomarker use and the choice of parameters for patient enrolment criteria when designing a clinical trial.

0

5

10

15

20

25

30

35

40

Dru

gs

(n)

Contraindication Dosage andadministration

Regulatorycompliance

Warnings andprecautions

2%

21%

14%

63%

Figure 5. Drugs identified in the US FDA ‘Table of Pharmacogenomic Biomarkers’ that reference a cytochrome P450 superfamily drug metabolizer in the prescribing information. Drugs are categorized by where the drug metabolizer biomarker appears in the prescribing information. For drugs where the biomarker appears in multiple sections, the categorization was based on a priority ranking based on the following order: contraindicated; dosage and administration section; warnings and precautions sections; and anywhere else; appearance in the drug interaction section was classified under warnings and precautions.



Executive summary

Background � The pharmaceutical industry is currently experiencing what has been referred to as an ‘innovation crisis’. To date, multiple strategies have been devised in an attempt to resolve the crisis – leveraging pharmacogenomics is one of them.

Biomarkers � Pharmacogenomics and the use of biomarkers have been identified by the US FDA as one of the main priorities moving forward.

Biomarker definitions � Experts and regulators have, by in large, reached a consensus definition of a biomarker.

� There still appears to be a discrepancy with regulators in the use of the terms ‘qualification’ and ‘validation’.

A risk-based paradigm of clinical trial design � Clinical trial risk plays a large role in the current crisis. We view this crisis through the lens of risk.

� In a risk-based paradigm of clinical trial design, we quantify risk based on historical performance of the disease area. This will allow for a fresh assessment of the utility of biomarkers in drug development and their risk mitigation benefits.

Biomarkers to the rescue? � A common view is that the use of biomarkers will increase clinical trial success rates and thus reduce risk; however, until recently, this has never been empirically examined.

� To date, two studies have been published that examine the potential implications of biomarkers on clinical trial success rates.

Barriers to the adoption of biomarkers by industry � Personalized medicine and biomarkers have strong public and academic support. However, there are those in industry that still have their reservations because of the perceived fear that revenues will be reduced as a result of a smaller market size.

Biomarkers in the context of FDA labeling � Accessing the FDA ‘Table of Pharmacogenomic Biomarkers’ website of on 8 January 2013 identified 105 approved drugs with 38 qualified biomarkers in their prescribing information. In total, 118 unique drug–biomaker combinations were present in the table.

Commercial risks of biomarker integration during Phases I–IV � The incorporation of a biomarker into the clinical development of a drug has varying consequences on the potential market. The result is often a decrease in the potential market and will depend on the specific disease area and biomarker.

The certolizumab pegol story: is there potential to reduce the treatment population unnecessarily? � Biomarkers that are incorporated during clinical trials do not always end up in the prescribing information.

Chronicling biomarker integration into clinical development � The late discovery of a biomarker that highlights substantial efficacy in a given subpopulation of patients or functions as a predictive factor in decreasing safety events can result in biomarker incorporation into later phases of clinical development.

Implications of risk in the case of cytochrome P450 superfamily drug metabolizers � Cytochrome P450 superfamily enzymes make up the majority of biomarkers that appear in FDA prescribing information.

� The incorporation of these biomarkers into drug development represents the current standard for drug manufacturers, as they have recognized the importance of the pharmacokinetic profile.

Conclusion � We view the innovation crisis through the lens of risk and argue that a risk-based paradigm of clinical trial design would allow for a fresh assessment of the utility of biomarkers in drug development and their potential to mitigate risk.

Financial & competing interests disclosureG Reid is employed in the pharmaceutical industry with GlaxoSmithKline. TA Bin Yameen is also employed in the pharmaceutical industry with AstraZeneca. J Parker has worked in the pharmaceutical industry and is a medical advisor to the hedge fund Burlington Capital. The authors

have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

ReferencesPapers of special note have been highlighted as:n of interestnn of considerable interest

1 Jack S, Alex AB, Helen B, Brian W. Diagnosing the decline in pharmaceutical R&D efficiency. Nat. Rev. Drug Discov. 11, 191–200 (2012).

2 Woodcock J, Woosley R. The FDA Critical Path Initiative and its influence on new drug development. Annu. Rev. Med. 59(1), 1–12 (2008).

n Broadly summarizes the current state of the pharmaceutical industry and makes clear the reasoning behind the US FDA Critical Path Initiative.

3 Adams C, Branter V. Spending on new drug development. Health Econ. 19(2), 130–141 (2010).

4 Paul SM, Mytelka DS, Dunwiddie CT et al. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov. 9(3), 203–214 (2010).



5 Allison M. Reinventing clinical trials. Nat. Biotechnol. 30(1), 41–19 (2012).

6 Dimasi JA, Hansen RW, Grabowski HG. The price of innovation: new estimates of drug development costs. J. Health Econ. 22(2), 151–185 (2003).

7 Dimasi JA, Grabowski HG, Vernon J. R&D costs and returns by therapeutic category. Drug Inform. J. 38(3), 211–223 (2004).

8 Mestre-Ferrandiz J, Sussex J, Towse A. The R&D Cost of a New Medicine. Office of Health Economics, London, UK (2012).

9 Hoekema A. Sharing risks and rewards: basis for a turnkey pharma-biotech alliance in osteoarthritis. Drug Discov. World 7, 53–58 (2007).

10 Ginsburg GS, Konstance RP, Allsbrook JS, Schulman KA. Implications of pharmacogenomics for drug development and clinical practice. Arch. Int. Med. 165(20), 2331–2336 (2005).

11 International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 409(6822), 860–921 (2001).

12 Venter JC, Adams MD, Myers EW et al. The sequence of the human genome. Science 291(5507), 1304–1351 (2001).

13 International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature 431, 931–945 (2004).

14 Roses AD. Pharmacogenetics and the practice of medicine. Nature 405(6788), 857–865 (2000).

15 Ginsburg GS, Willard HF. Genomic and personalized medicine: foundations and applications. Transl. Res. 154(6), 277–287 (2009).

16 Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin. Pharmacol. Ther. 69(3), 89–95 (2001).

17 Dancey JE, Dobbin KK, Groshen S et al. Guidelines for the development and incorporation of biomarker studies in early clinical trials of novel agents. Clin. Cancer Res. 16(6), 1745–1755 (2010).

18 Gosho M, Nagashima K, Sato Y. Study designs and statistical analyses for biomarker research. Sensors 12(7), 8966–8986 (2012).

19 Chau CH, Rixe O, McLeod H, Figg WD. Validation of analytical methods for biomarkers employed in drug development. Clin. Cancer Res. 14(19), 5967–5976 (2008).

20 Wagner JA. Overview of biomarkers and surrogate endpoints in drug development. Dis. Markers 18(2), 41–46 (2002).

21 Dimasi JA, Feldman L, Seckler A, Wilson A. Trends in risks associated with new drug development: success rates for investigational drugs. Clin. Pharmacol. Ther. 87(3), 272–277 (2010).

nn Provides a comprehensive analysis of clinical trial success rates and clinical-phase transition by therapeutic class and product type.

22 Arrowsmith J. Trial watch: Phase II failures: 2008–2010. Nat. Rev. Drug Discov. 10(5), 328–329 (2011).

23 Arrowsmith J. Trial watch: Phase III and submission failures: 2007–2010. Nat. Rev. Drug Discov. 10(2), 87 (2011).

24 Parker JL, Lushina N, Bal PS, Petrella T, Dent R, Lopes G. Impact of biomarkers on clinical trial risk in breast cancer. Breast Cancer Res. Treat. 136(1), 179–185 (2012).

nn Examines the clinical trial success rates in breast cancer relative to the presence of the well-established HER2 biomarker.

25 Osborne BJW, Kaul R, Parker JL. Drug development risk in HIV-1 clinical trials: the effect of drug class. J. Pharmaceutical Health Services Res. 2(4), 211–216 (2011).

26 Jayasundara KS, Keystone EC, Parker JL. Risk of failure of a clinical drug trial in patients with moderate to severe rheumatoid arthritis. J. Rheumatol. 39(11), 2066–2070 (2012).

27 Parker JL, Clare Kohler J. The success rate of new drug development in clinical trials: Crohn’s disease. J. Pharm. Pharm. Sci. 13(2), 191–197 (2010).

28 Tenuta A, Klotz L, Parker JL. Clinical trial risk in castration resistant prostate cancer: immunotherapies show promise. BJU International (In Press).

29 Falconi A, Lopes G, Parker JL. Biomarker and receptor targeted therapies reduce clinical trial risk in non-small cell lung cancer. J. Thorac. Oncol. (In Press).

30 Parker JL, Zhang ZY, Buckstein R. Clinical trial risk in non-Hodgkin’s lymphoma: endpoint and target selection. J. Pharm. Pharm. Sci. 14(2), 227–235 (2011).

31 Ledford H. Drug candidates derailed in case of mistaken identity. Nature 483(7391), 519 (2012).

32 Tan DS, Thomas GV, Garrett MD et al. Biomarker-driven early clinical trials in oncology: a paradigm shift in drug development. Cancer J. 15(5), 406–420 (2009).

33 Lesko L. Paving the critical path: how can clinical pharmacology help achieve the vision? Clin. Pharmacol. Ther. 81(2), 170–177 (2007).

34 Bienz-Tadmor B, Dicerbo PA, Tadmor G, Lasagna L. Biopharmaceuticals and conventional drugs: clinical success rates. Biotechnology 10(5), 521–525 (1992).

35 Struck MM. Biopharmaceutical R&D success rates and development times. A new analysis provides benchmarks for the future. Biotechnology 12(7), 674–677 (1994).

36 Dimasi JA. Success rates for new drugs entering clinical testing in the United States. Clin. Pharmacol. Ther. 58(1), 1–14 (1995).

37 Gosse ME, Dimasi JA, Nelson TF. Recombinant protein and therapeutic monoclonal antibody drug development in the United States from 1980 to 1994. Clin. Pharmacol. Ther. 60(6), 608–618 (1996).

38 Dimasi JA. Risks in new drug development: approval success rates for investigational drugs. Clin. Pharmacol. Ther. 69(5), 297–307 (2001).

39 Kola I, Landies J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov. 3, 711–716 (2004).

40 Reichert J. Monoclonal antibodies as innovative therapeutics. Curr. Pharm. Biotechnol. 9(6), 423–430 (2008).

41 Hayashi K, Masuda S, Kimura H. Impact of biomarker usage on oncology drug development. J. Clin. Pharm. Ther. 38(1), 62–67 (2013).

nn Describes the clinical trial success rates and transitional probabilities of 908 antitumor agents relative to the use of a biomarker in clinical trials.

42 Hayashi K, Masuda S, Kimura H. Analyzing global trends of biomarker use in drug interventional clinical studies. Drug Discov. Ther. 6(2), 102–107 (2012).

43 Bloomgren G, Richman S, Hotermans C et al. Risk of natalizumab-associated progressive multifocal leukoencephalopathy. N. Engl. J. Med. 366(20), 1870–1880 (2012).

44 Hamburg MA, Collins FS. The path to personalized medicine. N. Engl. J. Med. 363(4), 301–304 (2010).

45 Rodrigues AD, Lin JH. Screening of drug candidates for their drug–drug interaction potential. Curr. Opin. Chem. Biol. 5, 396–401 (2001).

46 Deverka PA, Vernon J, McLeod HL. Economic opportunities and challenges for pharmacogenomics. Annu. Rev. Pharmacol. Toxicol. 50, 423–437 (2010).

47 Trusheim MR, Berndt ER, Douglas FL. Stratified medicine: strategic and economic implications of combining drugs and clinical biomarkers. Nat. Rev. Drug Discov. 6(4), 287–293 (2007).

48 Kwak EL, Bang YJ, Camidge DR et al. Anaplastic lymphoma kinase inhibition in



www.futuremedicine.com

non-small-cell lung cancer. N. Engl. J. Med. 363(18), 1693–1703 (2010).

49 Neumann J, Zeindl-Eberhart E, Kirchner T, Jung A. Frequency and type of KRAS mutations in routine diagnostic analysis of metastatic colorectal cancer. Pathol. Res. Pract. 205(12), 858–862 (2009).

50 Rowley JD, Golomb HM, Dougherty C. 15/17 translocation: a consistent chromosomal change in acute promyelocytic leukaemia. Lancet 1(8010), 549–550 (1977).

51 List A, Dewald G, Bennett J et al. Lenalidomide in the myelodysplastic syndrome with chromosome 5q deletion. N. Engl. J. Med. 355(14), 1456–1465 (2006).

52 Holmes JA, Desmond PV, Thompson AJ. Does IL28B genotyping still have a role in the era of directacting antiviral therapy for chronic hepatitis C infection? J. Viral Hepat. 19, 677–684 (2012).

53 Sandborn WJ, Feagan BG, Stoinov S et al. Certolizumab pegol for the treatment of Crohn’s disease. N. Engl. J. Med. 357(3), 228–238 (2007).

54 Schreiber S, Khaliq-Kareemi M, Lawrance IC et al. Maintenance therapy with certolizumab pegol for Crohn’s disease. N. Engl. J. Med. 357(3), 239–250 (2007).

55 Vermeire S, Van Assche G, Rutgeerts P. C-reactive protein as a marker for inflammatory bowel disease. Inflamm. Bowel Dis. 10(5), 661–665 (2004).

56 Schreiber S, Rutgeerts P, Fedorak RN et al. A randomized, placebo-controlled trial of certolizumab pegol (CDP870) for treatment of Crohn’s disease. Gastroenterology 129(3), 807–818 (2005).

57 Ou SH. Crizotinib: a novel and first-in-class multitargeted tyrosine kinase inhibitor for the treatment of anaplastic lymphoma kinase rearranged non-small cell lung cancer and beyond. Drug Des. Dev. Ther. 5, 471–485 (2011).

58 Soda M, Choi YL, Enomoto M et al. Identification of the transforming EML4–ALK fusion gene in non-small-cell lung cancer. Nature 448(7153), 561–566 (2007).

59 Rikova K, Guo A, Zeng Q et al. Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer. Cell 131(6), 1190–1203 (2007).

60 Kwak EL, Cambidge DR, Clark JW et al. Clinical activity observed in a Phase I dose escalation trial of an oral c-met and ALK inhibitor, PF-02341066. J. Clin. Oncol. 27(15 Suppl.), Abstract 3509 (2009).

61 Cambidge DR, Bang YJ, Kwak EL et al. Progression-free survival (PFS) from a Phase I study of crizotinib (PF-02341066) in patients

with ALK-positive non-small cell lung cancer (NSCLC). J. Clin. Oncol. 29(Suppl.), Abstract 2501 (2011).

62 Crino L, Kim D, Riely GJ et al. Initial Phase II results with crizotinib in advanced ALK-positive non-small cell lung cancer (NSCLC): PROFILE 1005. J. Clin. Oncol. 29(Suppl.), Abstract 7514 (2011).

63 Innocenti F, Undevia SD, Iyer L et al. Genetic variants in the UDP-glucuronosyltransferase 1A1 gene predict the risk of severe neutropenia of irinotecan. J. Clin. Oncol. 22(8), 1382–1388 (2004).

64 Vanhoefer U, Harstrick A, Achterrath W, Cao S, Seeber S, Rustum YM. Irinotecan in the treatment of colorectal cancer: clinical overview. J. Clin. Oncol. 19(5), 1501–1518 (2001).

65 Ratain MJ. Irinotecan dosing: does the CPT in CPT-11 stand for ‘can’t predict toxicity’? J. Clin. Oncol. 20(1), 7–8 (2002).

66 Sargent DJ, Niedzwiecki D, O’Connell MJ, Schilsky RL. Recommendation for caution with irinotecan, fluorouracil, and leucovorin for colorectal cancer. N. Engl. J. Med. 345(2), 144–145; author reply 146 (2001).

67 Fuchs CS, Moore MR, Harker G, Villa L, Rinaldi D, Hecht JR. Phase III comparison of two irinotecan dosing regimens in second-line therapy of metastatic colorectal cancer. J. Clin. Oncol. 21(5), 807–814 (2003).

68 Ando Y, Saka H, Ando M et al. Polymorphisms of UDP-glucuronosyltransferase gene and irinotecan toxicity: a pharmacogenetic analysis. Cancer Res. 60(24), 6921–6926 (2000).

69 Marcuello E, Altes A, Menoyo A, Del Rio E, Gomez-Pardo M, Baiget M. UGT1A1 gene variations and irinotecan treatment in patients with metastatic colorectal cancer. Br. J. Cancer 91(4), 678–682 (2004).

70 Rouits E, Boisdron-Celle M, Dumont A, Guerin O, Morel A, Gamelin E. Relevance of different UGT1A1 polymorphisms in irinotecan-induced toxicity: a molecular and clinical study of 75 patients. Clin. Cancer Res. 10(15), 5151–5159 (2004).

71 Toffoli G, Cecchin E, Corona G et al. The role of UGT1A1*28 polymorphism in the pharmacodynamics and pharmacokinetics of irinotecan in patients with metastatic colorectal cancer. J. Clin. Oncol. 24(19), 3061–3068 (2006).

72 Carlini LE, Meropol NJ, Bever J et al. UGT1A7 and UGT1A9 polymorphisms predict response and toxicity in colorectal cancer patients treated with capecitabine/irinotecan. Clin. Cancer Res. 11(3), 1226–1236 (2005).

73 Hoskins JM, Goldberg RM, Qu P, Ibrahim JG, McLeod HL. UGT1A1*28 genotype and irinotecan-induced neutropenia: dose matters. J. Natl Cancer Inst. 99(17), 1290–1295 (2007).

74 Punt CJ. Irinotecan or oxaliplatin for first-line treatment of advanced colorectal cancer? Ann. Oncol. 16(6), 845–846 (2005).

75 O’Connell MJ. Oxaliplatin or irinotecan as adjuvant therapy for colon cancer: the results are in. J. Clin. Oncol. 27(19), 3082–3084 (2009).

76 Danielson PB. The cytochrome P450 superfamily: biochemistry, evolution and drug metabolism in humans. Curr. Drug Metab. 3(6), 561–597 (2002).

77 Shenfield GM. Genetic polymorphisms, drug metabolism and drug concentrations. Clin. Biochem. Rev. 25(4), 203–206 (2004).

78 Rendic S, Dicarlo F. Human cytochrome P450 enzymes: a status report summarizing their reactions, substrates, inducers and inhibitors. Drug Metab. Rev. 29, 413–580 (1997).

79 Lewis DF, Eddershaw PJ, Dickins M, Tarbit MH, Goldfarb PS. Structural determinants of cytochrome P450 substrate specificity, binding affinity and catalytic rate. Chem. Biol. Interact. 115(3), 175–199 (1998).

80 Lewis DF, Hlavica P. Interactions between redox partners in various cytochrome P450 systems: functional and structural aspects. Biochim. Biophys. Acta 1460, 353–374 (2000).

81 Anzenbacher P, Anzenbacherova E. Cytochromes P450 and metabolism of xenobiotics. Cell. Mol. Life Sci. 58(5–6), 737–747 (2001).

82 Guengerich FP. Common and uncommon cytochrome P450 reactions related to metabolism and chemical toxicity. Chem. Res. Toxicol. 14(6), 611–650 (2001).

83 Guengerich FP. Mechanisms of drug toxicity and relevance to pharmaceutical development. Drug Metab. Pharmacokinet. 26(1), 3–14 (2011).

84 Lewis DF, Dickins M. Substrate SARs in human P450s. Drug Discov. Today 7(17), 918–925 (2002).

85 Tralau T, Luch A. ‘Drugs on oxygen’: an update and perspective on the role of cytochrome P450 testing in pharmacology. Expert Opin. Drug Metab. Toxicol. 8(11), 1357–1362 (2012).

n Summarizes and highlights the major challenges with cytochrome P450 superfamily matabolizers in drug development today, and also provides a look ahead to the future direction of cytochrome P450 testing.

n Websites101 US FDA. FDA’s Critcal Path Initiative.

www.fda.gov/ScienceResearch/SpecialTopics/CriticalPathInitiative/ucm076689.htm (Accessed 24 January 2013)

www.fda.gov/ScienceResearch/SpecialTopics/CriticalPathInitiative/ucm076689.htm

www.fda.gov/ScienceResearch/SpecialTopics/CriticalPathInitiative/ucm076689.htm



102 US FDA. Critical Path Opportunities Report. March 2006. www.fda.gov/downloads/ScienceResearch/SpecialTopics/CriticalPathInitiative/CriticalPathOpportunitiesReports/UCM077254.pdf (Accessed 6 Febuary 2013)

103 US FDA: Guidance for industry. E16 biomarkers related to drug or biotechnology product development: context, structure and format of qualification submissions (2011). www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM267449.pdf

104 US FDA: Guidance for industry: pharmacogenomic data submissions (2005). www.fda.gov/downloads/regulatoryinformation/guidances/ucm126957.pdf

105 US FDA. Table of Pharmacogenomic Biomarkers in Drug Labels. www.fda.gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm083378.htm (Accessed 8 January 2013)

106 Pfizer: CAMPTOSAR – product monograph. 2013 (1 March 2009)

http://labeling.pfizer.com/ShowLabeling.aspx?id=533 (Accessed 8 January 2013)

107 US FDA: NDA 20-571/S-026. 2013 (1 March 2005). www.accessdata.fda.gov/drugsatfda_docs/appletter/2005/020571s026ltr.pdf (Accessed 8 January 2013)

108 US FDA: NDA 20-571 – Camptosar final label. 2013 (1 March 2005). www.accessdata.fda.gov/drugsatfda_docs/label/2005/020571s026lbl.pdf (Accessed 8 January 2013)

www.fda.gov/downloads/ScienceResearch/SpecialTopics/CriticalPathInitiative/CriticalPathOpportunitiesReports/UCM077254.pdf




www.fda.gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm083378.htm



Download - Impact of biomarkers on clinical trial risk

Top Related