WHITEPAPER No Drug Designed For Me - Elsevier · 2015-11-18 · No Drug Designed For Me. ... The term “personalized medicine” entered mainstream rhetoric enveloped in the hype

No Drug Designed For Me

MORE EFFECTIVE THERAPIES FOR SMALLER GROUPS OF PATIENTS New targeted medications have revealed the value of matching patient and therapy, but the narrow indications for such drugs call for big changes in the way drugs are developed.

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The current embodiment of personalized medicine is not tailored to the individual. Instead, it hinges on delivering a dedicated drug to a precisely defined subgroup of patients.

THE REALITY OF “PERSONALIZED” MEDICINEThe term “personalized medicine” entered mainstream rhetoric enveloped in the hype around the genomic revolution. The idea of “a drug designed for me” grew out of the prediction that rapidly decreasing DNA sequencing costs would spur mass production of genomic data that could be correlated to information on the demographics, medical histories and drug responses of entire populations (1). Sophisticated computer algorithms would then analyze trends and connections among tens of thousands of patients to deduce relevant treatment options for an individual based on similarities in genomic profiles.

“A drug designed for me” may have been an optimistic forecast of future medicine.


Reality looks a bit different. The current embodiment of personalized medicine is not tailored to the individual. Instead, it hinges on delivering a dedicated drug to a precisely defined subgroup of patients. Thus, rather than pinpoint beneficial therapies for an individual based on data from an entire population as forecasted, it extracts a biologically defined subgroup of patients from the population and addresses their medical need. An individual then qualifies for the developed treatment by testing for the biological characteristic that defines the subgroup. This characteristic or “biomarker” is usually the presence of a gene variant of specific protein that impacts how individuals respond to therapy and is often detected with a companion diagnostic — an assay coupled to the drug. The U.S. National Research Council recognized the discrepancy and introduced a new term to better reflect reality: precision medicine (3).

Although the insights hoped for by the first enthusiasts of these tailored therapies have not materialized, drug approvals in the last decade attest to a transformation in pharmaceutical research and development (Figure 1). Precision medicine departs from the “blockbuster” model of creating one medication for all patients by focusing the clinical development of a candidate drug on patients predicted to respond well to therapy. Some of the medicines developed under this rubric have astounding efficacies and are encouraging alternatives for medical areas, such as non-small cell lung cancer, that have limited treatment options.

Building an arsenal of medications matched to those patients who benefit the most is a step toward tailored therapies, however, the predominant approach to drug development threatens to limit progress in precision medicine. Drugs continue to be designed in a reductionist

approach that isolates the interaction between a therapeutic compound and a single biological molecule, even though the resulting drug is administered to organisms where molecules operate in a complex orchestration of DNA, RNA, proteins, steroids, metabolites and more. Experience has shown that redundancy built into networks of biological molecules can eliminate the impact of a drug on a single target and even render pathogens or tumors resistant to medication (4). This reductionist methodology also permeates the clinical development of precision medicines. Currently, the decision to prescribe a targeted drug is based on testing for one biomarker linked to the disease, but as knowledge of drug response determinants improves, single-biomarker diagnostics will quickly become obsolete.

Overcoming these limitations will require adopting and exploring a more comprehensive picture of drug action in complex biological systems. The single drug-target interaction will need to be augmented with information about the role and susceptibility of the target molecule within the molecular networks or pathways it serves. Compounds under development will need to be conceived as multi-target modulators of a complete system of molecules, instead of individual system components. Future precision medicines will emerge from the intercept of a ballooning number of data sources and types. Extracting insights from such a complex information landscape to effectively inform drug design will demand new experimental methods and analysis techniques. In short, we will see that the continued evolution of precision medicine will depend on an overhaul of the conceptual framework underlying drug development. But first, let us examine the history of treatment options for non-small cell lung cancer (NSCLC) to trace the progress of precision medicine and reveal looming hurdles.

Oncology

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Figure 1. FDA-approved drugs with pharmacogenomic biomarkers in their labeling, as of April 2015. The pharmacogenomic information provided for some, but not all, of these drug includes actions to be taken based on the presence or absence of the listed pharmacogenomic biomarker. Not included are non-human genetic biomarkers or those used only for diagnosis (2).


NOT ALL PATIENTS ARE ALIKE

The idea that a drug is more effective in patients with a particular gene or gene mutation was not new in 2004. Already in the 1970s, the correlation between treatment response to the cancer drug tamoxifen (Novaldex®, AstraZeneca) and high expression of the estrogen receptor gene led to the suggestion to select patients for treatment on the basis of this overexpression (13). Furthermore, the first precision medicine to be approved by the FDA with a companion diagnostic for patient selection came in 1998: the breast cancer drug trastuzumab (Herceptin®, Genentech) and HER2 assay HercepTest® (Dako).

PRECISION THERAPIES FOR NON-SMALL CELL LUNG CANCERLung cancer is the leading cause of cancer-related deaths in most countries, with 156,953 deaths in the US and 257,768 expected deaths in the European Union in 2011 (5,6). Traditionally classified based on the appearance of cancerous cells, the broad group non-small cell lung carcinoma (NSCLC) accounts for approximately 85% of all lung cancer cases (7). In the early 1990s, treatment options for NSCLC were limited and median survival was 2-4 months. The introduction of increasingly sophisticated chemotherapies increased survival to 12-14 months (8). The medical need has been and remains great. A handful of successful precision medicines to treat NSCLC have recently entered the market and, based on active clinical trials, the number of these drugs is likely to increase quickly over the next years (Figure 2).

In 2003, the FDA approved the drug gefitinib (Iressa®, AstraZeneca) to treat NSCLC, a welcome alternative to rounds of radiation or cytotoxic chemotherapy. Gefinitib is an EGFR tyrosine kinase inhibitor that blocks the activity of the epidermal growth factor receptor (EGFR). EGFR sits in the cell membrane and, when activated, stimulates a network of cellular molecules — a signaling pathway — that promotes cell growth and proliferation. A second EGFR tyrosine kinase inhibitor, erlotinib (Tarceva®, Genentech/OSI Pharmaceuticals), was approved by the FDA in 2004.

Oncologists treated patients with these drugs but were frustrated by the fact that many did not respond and there was no apparent pattern to predict who would benefit. Analyses suggested that

gefitinib was more likely to be effective in Japanese patients, in never smokers, and in patients with a very specific carcinoma subtype (10), but experience showed that those classifications were not precise. Three research groups identified the culprit in 2004. Specific mutations of the gene for EGFR were found with striking predominance in tumors that were sensitive to the two drugs and in samples from Japan (10-12). The effectiveness of the two drugs appeared to depend on the presence of specific genetic aberrations in a patient’s tumor. Several in vitro studies showed that these mutations increase EGFR activity, likely leading to excessive cell growth and proliferation (i.e., tumor development). As EGFR tyrosine kinase inhibitors, gefitinib and erlotinib block this abnormal signal.

In the last decade, precision medicines have afforded new treatment options and renewed hope for thousands of non-small cell lung cancer patients.


New drug candidate in clinical trials

Approved drugs in clinical trials for repurposing

ALK

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Figure 2. A sample of targeted therapies in clinical trials for non-small cell lung cancer, as of January 2014.ALK = Anaplastic lymphoma kinase; PI3K = phosphoinositide 3-kinase; AKT = protein kinase B; Plk1 = polo-like kinase 1; BRAF = serine/threonine-protein kinase B-raf; Chk1 = checkpoint kinase 1; MEK = mitogen-activated protein kinase kinase; PARP = poly (ADP-ribose) polymerase; MET = hepatocyte growth factor receptor; IGF-1R = insulin-like growth factor 1 receptor; EGFR = epidermal growth factor receptor; VEGFR = vascular endothelial growth factor receptor; FGFR = fibroblast growth factor receptor; Bcl-2 = B-cell lymphoma (a cell death regulator); HIF-1 α = hypoxia-inducible factor 1-alpha; BCR-ABL = protein arising from chromosome fusion between Abelson (Abl) tyrosine kinase gene and breakpoint cluster (Bcr) gene; mTOR = mechanistic target of rapamycin. Data extracted from Simon & Somaiah, 2014 (9).


What was new was the realization that low efficacies of cancer drugs might be attributed to the heterogeneity of the tested patient population, which essentially dilutes the strong therapeutic effect that a drug might have on a specific patient subgroup (14). Prior to this realization, drug development assumed that one drug works for all patients diagnosed with a particular disease. This “blockbuster” model has been, with few exceptions, notoriously unsuccessful in oncology. An FDA publication highlights a 2001 study reporting an average efficacy of cancer drugs — a measure of the capacity for beneficial change of a given intervention — of only 25% (15). Another study in 2004 reported that only 5% of available drugs are effective in patients whose cancer has metastasized (16).

This realization marks a milestone in the clinical development of drugs. The blockbuster approach, with its assumption “one drug fits all,” began to crumble and a new approach aiming to deliver “the right drug to the right group of patients” emerged as a more meaningful and potentially more successful way of bringing new drugs on to the market (Figure 3). Information about patient traits that impact response to a drug presented an opportunity to dodge the high attrition rate that has plagued the pharmaceutical industry for decades. The benefit of focusing the clinical development of a drug is epitomized by the approval history of the highly effective precision medicine for NSCLC, crizotinib (Xalkori®, Pfizer).

Molecular biology & recombant DNA technology enter pharmaceutical R&D

Biological pathways and tumor evolution trump single targets

Recognition that patient populations are heterogeneous

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BLOCKBUSTERTrial and error-based drug design

BLOCKBUSTERTarget-oriented drug design

Network-oriented drug design and development

PRECISION MEDICINETarget-oriented drug design

“one drug fits all”“the right drug for the right group of patients”

“the right drug for the right patient at the right time and in the right dose”

Figure 3. The evolution of drug development models, from “blockbuster” to precision medicine and into the future. Each progressive step is informed by increasing amounts of data, first from a datastream with information on disease and drug mechanisms, then from a second datastream with information on patient characterization. The complexity of the data will also increase as drug design and development shifts focus from single targets to complete biological networks.


CHOOSE YOUR POPULATION WISELY

In 2007, a team of researchers in Japan identified another aberration relevant to NSCLC: a rearrangement in chromosomes that fuses together portions of the ALK and EML4 genes (17). The result is an ALK receptor with an appended EML4 protein. The fused EML4-ALK receptor is always active and promotes tumor cell proliferation and survival. At the time, Pfizer was testing a candidate drug as a MET kinase inhibitor. MET is a receptor commonly expressed in epithelial cells (which are abundant in lung tissue) that activates signaling pathways for cell proliferation and migration during embryo development, wound healing and tissue regeneration. These pathways are often activated in different cancer types. Pfizer’s candidate drug had been tested in the lab against a panel of 120 kinases, a group of molecules with the same activity mechanism as MET. The drug had proven to inhibit ALK. When results on the EML4-ALK fusion protein were published, a few centers participating in Pfizer’s phase I clinical trial were able to develop tests for the presence of the rearranged ALK gene. When the first two ALK-rearranged patients benefitted from the candidate drug, Pfizer shifted the focus of its clinical trial (18).

The drug, later named crizotinib, was approved by the FDA in 2011 in a fast-tracked and unprecedented decision based on data from only phase I and phase II clinical trials. Crucial to the approval was the use of a test to identify the subgroup of NSCLC patients who would benefit from crizotinib. The test uses fluorescent probes to detect the presence of the rearranged ALK gene and, under collaboration with Abbott Molecular, transitioned rapidly from a lab developed test to the approved companion diagnostic Vysis® ALK Break Apart FISH Test. Response rates of this selected group of patients were an astounding 50-60% (19). One post-approval phase III clinical trial tested crizotinib in EML4-ALK-positive patients who had undergone chemotherapy and demonstrated significantly better response and survival rates over continued chemotherapy. A further clinical trial confirmed a significant benefit to previously untreated EML4-ALK-positive patients when crizotinib is used as first-line treatment compared to chemotherapy (20).

What was unprecedented about this approval was that it was based on data from only 255 patients with NSCLC in early phase clinical trials (19), and that a diagnostic test was used at this early stage to select EML4-ALK-positive patients to be included in the trial. Essentially, having identified a biological characteristic linked to NSCLC that predicted a positive response to the drug, developers whittled down the test population to only those patients who possessed that characteristic. The result was dramatically high response rates. A similar scenario led to the accelerated FDA approval of ceritinib (Zykadia®, Novartis) in 2013. That decision was based on a phase I clinical trial that enrolled 163 patients with metastatic NSCLC whose disease progressed while on crizotinib (20). Although ceritinib was

not approved with a companion diagnostic, the patient subgroup that benefits from this drug was preselected: (a) as ELM4-ALK-positive patients, a requirement for treatment with crizotinib, and (b) as patients whose disease progressed despite treatment with crizotinib. Once again, selection of a subgroup of patients for the early phase clinical trial led to extraordinary response rates (>50%; 20).

Dr. Jean Cui, attributed with the invention of crizotinib, calls the strategy of precisely defining the target patient group for a medication a turning point in cancer drug development. She explains, “This model was considered a specialized case […]. Now it is a widely used model that is generating an arsenal of precision medicines […] that exhibit extremely high efficacies in small subgroups of the patient population.” The success of targeting selected patient subgroups has driven even established medications to be equipped with a companion diagnostic to inform treatment decisions. The cobas® EGFR Mutation Test from Roche was approved by the FDA in 2013 as a companion diagnostic for erlotinib, 9 years after the drug was introduced into the market. The test specifically detects two different mutations of the EGFR gene that results in the hyperactive form of the receptor that erlotinib inhibits. The clinical trial that led to the approval of the cobas test showed that 65% of EGFR-activating mutation-positive NSCLC patients who took erlotinib exhibited significant tumor shrinkage compared to only 16% of patients who received chemotherapy (21). AstraZeneca announced in 2014 that it has partnered with QIAGEN to develop a companion diagnostic for gefitinib based on tumor DNA circulating in the patient’s bloodstream, a welcome advancement over tests that require a lung tissue biopsy (22).


LOOKING INTO THE FUTURE: A SERIAL ARMS RACE AGAINST CANCER

The astounding response rates elicited by precision medicines like crizotinib, ceritinib and erlotinib after being coupled to the cobas diagnostic test, stem from augmenting the information that flows into the development of a drug. The use of data on the structure and functional role of a molecular target to design and optimize compounds to bind it has been staple in drug development since molecular biology and recombinant DNA technologies penetrated pharmaceutical research in the 1980s. With the advent of precision medicine, information input was expanded to include data on genetic variation that impacts patient response, resulting in

a more precise clinical development strategy. Thus, precision medicine is informed on one hand by data on molecular targets for drug design and, on the other hand, by data on patient characterization for drug indication. The benefit of converging these two information streams may, however, be short-lived. To begin with, cancer is a dynamic disease and is already limiting the utility of available precision medicines. Furthermore, the very essence of precision medicine — efficacious drugs for specific subgroups of the patient population — may lead this approach to drug development toward a dead end.

Non-small cell lung cancer patients respond initially very well to gefitinib and erlotinib but almost invariably develop resistance after 10-12 months of treatment (8). Researchers have identified a surprising number of different mechanisms underlying acquired resistance. A secondary mutation of the EGFR gene called T790M has been identified as the cause of acquired resistance to gefitinib and erlotinib in approximately 50% of patients. The mutation appears to increase the affinity of EGFR for its natural ligand (adenosine triphosphate, or ATP) making it less likely to bind gefitinib or erlotinib (23). Another 20% of patients acquire resistance through increased activity of MET, which activates different signaling pathways in the cell, but has the same end effect of excess cell growth and proliferation. At least 3 other mutations of EGFR have been implicated in acquired resistance, as well as mutations in BRAF and increased gene copy number of HER2, both well-known cancer-related genes (23). Patients treated with crizotinib also relapse after a period of sensitivity to the drug. Again here, resistance mechanisms are diverse. Initial studies show that nearly 25% of patients have secondary mutations in the ALK gene or an increase in the copy number of the EML4-ALK fusion gene. Roughly 50% of patients bypass the inhibited ALK pathway by activating other pathways, including EGFR. Finally, mutations in the cancer-driver gene KRAS have also been implicated in conferring resistance to crizotinib (23).

Knowledge of these resistance mechanisms has guided the development of next-generation drugs that target known culprit aberrations. For example, promising results from two drug candidates, CO1686 and AZD9291, were presented at the 2014 annual meeting of the American Society of Clinical Oncology. Both drugs specifically target and inactivate the T790M mutation responsible for resistance to gefitinib and erlotinib (23). Another example is ceritinib, which was developed for NSCLC patients with acquired resistance to crizotinib and has proven to be 20 times as potent as crizotinib in its ability to inhibit the mutated ALK receptor (20). Finally, alectinib (Roche) is a drug approved in Japan in 2014 for NSCLC patients with the EML4-ALK fusion gene that was granted Breakthrough Therapy Designation by the FDA in 2013 for NSCLC patients who progress on crizotinib (24).

The speed with which second-generation drugs are entering the clinical setting for patients with resistant tumors is evidence that lessons have been learned from first generation drugs and that genomic data from patients are informing the development of the next generation. However, records of widespread acquired resistance and above all, discovery of such diverse mechanisms, raises the question whether such an “arms race” against a never-ending series of acquired resistances is tenable. Can the pharmaceutical industry keep pace if the susceptibility window for each drug is only months? And given the diversity in resistance mechanisms, can enough drugs be developed to address all forms of resistance, that is, to meet the needs of all patient subgroups?


The rubric “one drug, one target, one patient group” will lead precision medicine into a scientific and financial cul-de-sac.

LIMITATIONS OF CURRENT DRUG DEVELOPMENT APPROACHESPrecision medicines for cancer are developed under the assumption that tumors arise from a single dominant molecular aberration that causes a signaling pathway to run amok, and that knocking the culprit molecule out of action is sufficient to halt tumor growth. This assumption fits well into the reductionist, target-oriented approach to drug design that prevails in the pharmaceutical industry. Under this approach, the culprit molecule is isolated from the influence of other molecules and from microenvironmental conditions present in a cell. A compound is then designed to optimally bind and modulate it. Thus, each drug is developed for one target alone. In the case of precision medicine, additionally focusing clinical development to address a specific patient subgroup results in a linear “one drug, one target, one patient group” rubric for each development program seeking to bring a medication to market.

The long-term repercussions of this linear rubric are best appreciated in the serial development of next generation drugs to manage acquired resistance to cancer treatments. Creating second-generation drugs that address each mechanism of resistance in isolation, as currently done, makes each new drug relevant for only a subset of patients treated with the first-generation drug. So, the T790M inhibitors CO1686 and AZD9291 address the medical need of half of the NSCLC patients who test positive for EGFR-activating mutations. Drawing this logic into subsequent rounds of acquired resistances leads to the conclusion that every new defensive line of drugs will be indicated for smaller and smaller patient subgroups. Some second-generation drugs, such as ceritinib, are proving to be effective against multiple resistance mechanisms or even as first-line therapies (23). However, the shrinking size of patient subgroups that benefit from a medication is not restricted to acquired resistance. This trend is also observed among drugs targeting different molecular aberrations implicated in lung and in breast cancer (Figure 4).

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In clinical trialsApproved Figure 4. Frequency of mutations targeted by approved and candidate drugs to treat breast and lung cancer. Over time, the size of patient groups that benefit from approved and future cancer drugs decreases, as the frequency of target aberrations is lower. Percentages of mutations in genes for EGFR, KRAS, MET, BRAF and PIK3 represent the average of the highest reported frequency in different ethnic groups. Data extracted from Jørgensen 2011 (14) and El-Telbany & Ma 2012 (25).


PROGRESSING INTO A CUL-DE-SAC

Fact is, cancer-driving molecular aberrations are highly diverse and those that have been identified until now are most often present in small numbers of patients. Furthermore, changes in tumors over time (e.g., resistance development) means that future drugs developed under the rubric “one drug, one target, one patient group” will be relevant for a shrinking number of individuals. Continuing to work under this rubric will lead precision medicine into a scientific and financial cul-de-sac. First, if the trend of shrinking patient subgroups holds, limiting test populations in clinical trials to only subjects predicted to benefit from a drug will make it increasingly difficult to find the number of patients needed to demonstrate drug efficacy and safety. Dr. Cui named this one of the biggest challenges facing precision medicine. The very high efficacies of crizotinib and ceritinib justified FDA approval of both drugs based on responses from a small number of patients (255 and 163, respectively), but data continued to be collected in post-market trials to confirm drug efficacy and safety (19). The clinical utility of precision medicines with lower efficacies will be less clear cut and demand larger sample sizes to demonstrate statistical significance. Furthermore, precision medicine will eventually expand into other medical areas where patients and physicians are less likely to use new drug therapies without thorough safety assessment.

How will clinical trials in those areas garner sufficient data to support claims about drug efficacy and safety? Second, the business model of current precision medicine is unsustainable into the future. A drug that can be prescribed to only a small subgroup of patients will require a large price tag to recover development costs. The smaller the patient subgroup, the lower the return. Unless development costs can be reduced, continued investment in precision medicine will not be warranted; especially in light of extended clinical trials needed to recruit a required minimum number of patients.

Sifting through the hype around precision medicine, it is important to acknowledge that successes rest on the precise selection of patients to receive treatment, but little has really changed about the way drugs are designed and optimized. These are still single-target drugs that are matched to fortuitously identified patient subgroups. That is, precision medicines are an improvement over other medications only because they are precisely indicated for the right patients, and not because they are created to tackle diseases with innovative modes of action. Opening a new path out of the cul-de-sac of ever shrinking patient subgroups will require a conceptual and practical overhaul in the way drugs are created and clinically evaluated.


SOLUTIONS MAY EMERGE FROM A SYSTEMS-BASED APPROACH

NETWORKS TRUMP SINGLE TARGETS

Target-oriented drug design and optimization fits well into the industrialized workflows of pharmaceutical research and development. Examining compound-target pairs in isolation generates well-understood measures of affinity (likelihood of a drug binding a target) and potency (strength of impact of a drug on a target) that can be evaluated in standardized analyses of target binding and modulation. This reductionist approach, however, does not reflect the complexity of drug action in organisms (4) or the dynamic nature of cancer. The resistance mechanisms observed in NSCLC highlight the fallacy of assuming that cancer can be treated by knocking out a single aberrant molecule. First, cell function is based on the activity of signaling pathways that are highly redundant, interconnected networks of multiple relevant molecules. Shutting down one aberrant pathway causing tumor growth may lead to compensatory activity in another. Second, the genetic profile of a tumor changes over time. New aberrations in tumor cells may render a target molecule unsusceptible to a drug (Figure 5).

Tumor growth1 Tumor growth2

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Figure 5. Mechanisms that may reduce the effect of a therapeutic drug. (1) The graphic shows a simplified, hypothetical network of proteins that regulate cellular functions which, when excessively stimulated, cause tumor growth. A mutation to the gene that codes for protein X leads to increased activity of the signaling pathway XYZ, causing tumor growth. Normal activity of signaling pathway BCD activates those cellular functions but only to the extent needed in the cell, thereby not causing tumor growth. The mutated X also inhibits protein A (indicated by the T-line). (2) A drug is designed to bind and inhibit X. As a result, pathway XYZ is shut down. However, inhibition of A is concurrently removed and as a result, A stimulates C and Z, leading to tumor growth. The therapeutic drug is rendered ineffective. (3) Alternatively, a mutation may arise in the gene for B causing it to become overactive and thus, leading to overstimulation of pathway BCD. (4) Another alternative is that a secondary mutation arises in X, which renders it unsusceptible to the therapeutic drug.


The future of precision medicine lies in creating better therapies. For example, improved precision medicines for cancer should deliver a powerful therapeutic effect while simultaneously circumventing acquired resistance. The most promising approach to creating such drugs is to modulate complete networks of interacting biological molecules, either by designing promiscuity into a drug so it “hits” several critical components of a network, or by developing an arsenal of single-target drugs and understanding how these interact with one another to create effective combination therapies (26). Data from isolated drug-target pairs is insufficient to inform this multimodal approach. Instead, solutions may emerge from a new conceptualization of the informational space relevant to medicinal chemistry. This informational space abandons individual drug-target interactions to instead incorporate data on drug interactions with multiple molecular targets, on target interactions with multiple compounds, on complex interactions between molecules and molecular networks relevant to disease, on interactions between compounds, and on the role of microenvironmental conditions in modulating all of the above.

This augmented informational space is currently unfolding as results from isolated drug-target interactions are collected into larger frameworks of complete signaling pathways, and as researchers work out methodologies to analyze and interpret the vast bodies of genomic, epigenomic, transcriptomic, proteomic, phosphoproteomic, and metabolic data available in multiple scientific repositories (27). Systems or network biology, for example, offers analysis tools to explore, probe and understand systems of multiple components, which for drug development can range from a single signaling pathway, to multiple interlinked pathways, networks of correlated molecules, specific cellular functions, and more. Systems biology analyzes the relationships among the various components of a system to uncover properties that emerge from all interactions rather than from the inventory of individual components. To this end, systems biology integrates experiments in iterative cycles of mechanistic modeling and in silico simulations to generate a continuously improved representation of a biological system (28).

In the case of drug development, mechanistic models and assays that measure the effects of altering the activity of a system can enable researchers to examine the response of signaling pathways to perturbations elicited by candidate drugs. Detailed models simulate all interactions between molecules of a pathway and the overall effect of stimulating the pathway. By systematically altering properties of components or interactions (e.g., inhibiting a component receptor), researchers can explore the sensitivity of the pathway to such perturbations and pinpoint interactions that are crucial to disease or identify a handful of pathway components that can be modulated synergistically to achieve a desired effect. Subsequently, assays that measure the outcome of the pathway serve as an empirical testing ground to scrutinize simulation predictions and generate feedback for adapting the model. In this way, drug developers can examine “weak spots” in a signaling pathway that may serve as targets for a more comprehensive modulation of the entire system.

Expanding the scope of information relevant to drug design and optimization will require visualizing, evaluating, and interpreting large amounts of unexplored data. Systems-based mathematical strategies require no prior knowledge of how components in a system interact and yet enable data-driven simplification to extract patterns and properties from multimodal datasets that can serve as basis for modeling. For example, weighted correlation network analysis, can assist in summarizing clusters of molecules with linked functions and reveal new signaling pathways that may be relevant to a disease but would have otherwise gone unnoticed because connections were not obvious from looking at individual system components (29). Dr. Herbert Köppen, a veteran of the pharmaceutical industry, advocates integrating network biology approaches into drug development processes and points out that “implementing those tools and ensuring that they are a good proxy of the human body will require intensive work. The technologies are already available, but the workflows need to be developed and then integrated into the framework of pharmaceutical research.”


The higher-level methodologies used in systems biology are promising tools and approaches for drug design and optimization but ineffective without the expertise to use them. Dr. Scott Lusher has ample experience in the development of IT solutions for data-laden scientific research. He explains, “the features and analysis power of any technology are secondary to the interpretation of data, and this is still done by people. A change in mentality is also required. Medicinal chemists, biologists, pharmacologists, and all other team members will need to work with, share, trust and interpret data differently.” Drug developers will need to explore and interpret a highly complex picture of correlated molecular targets interacting with multiple therapeutic compounds in a heterogeneous and changing physiological environment to extract key insights relevant to a drug’s design. They will need to operate at the intercept of a growing number of data sources and experimental methods. They will need to adopt new models for data acquisition, evaluation and interpretation. They will need to work closely with computational scientists and mathematicians (30). Ultimately, however, the resulting collaborative and cross-disciplinary teams will translate large amounts of correlated, diverse and often conflicting data into insights that drive the creation of not just more precise, but better drugs.

FROM COMPANION TO GUIDING DIAGNOSTICS

The expanded informational space supporting the design of better drugs must also support their clinical development. Diagnostic tools to match patient and drug therapy will require an update as knowledge of drug action in complex systems improves and lists of relevant biomarkers grow. The current “one drug, one target, one patient group” rubric will lead to a cluttered landscape of highly targeted cancer therapies coupled to companion diagnostic tests that measure single biomarkers relevant to single drugs. The low likelihood of a patient testing positive for any one of those biomarkers raises questions about the practicality (and cost) of such a disjointed array of precision medicine therapies. Furthermore, a more sophisticated characterization of patients raises questions about the suitability of standard clinical trial models to support precision medicine.

With increasingly detailed characterization of a patient population, the clinical relevance of a diagnostic strategy cannot continue to rest on a single biomarker, as it does now. One possibility is that diagnostic tests will evolve into “multiplexed panels or arrays that deliver information on multiple relevant biomarkers,” as predicted by Dr. Jan Trøst Jørgensen, an expert on companion diagnostics. By providing a more

sophisticated profile of the patient rather than just a single-point reference about the patient’s susceptibility to a drug, the focus of such multiplexed diagnostic tests will shift from drug to patient. That is, the “companion diagnostic” coupled to a drug will evolve into the “guiding diagnostic” that chaperones a patient through his or her medical journey. As such, the test should measure several biomarkers that inform a decision scheme to rule out inappropriate or superfluous therapies and opt for relevant ones. The type of biomarkers used in these tests may also diversify. Although to date diagnostic tests focus on the presence of genetic mutations or particular protein forms, the repertoire of biomarkers under research is expanding to include non-coding RNAs, metabolites, and even patterns in functional medical imaging (17), all of which may be highly informative for therapy decisions. The result of this multimodal approach, Jørgensen predicts, will be a forced decoupling of drug and companion diagnostic. How multiplexed diagnostics will be packaged to ensure that they retain their function of informing the prescription of one or more drugs remains to be determined, but will likely depend on the future configuration of clinical trials.


Standard clinical trial design currently used for precision medicine approvals may not practically accommodate the complexity of information needed to support multiplexed characterization of patients. First, current design restricts the number of subjects recruited into a trial. Consequently, a trial generates less data on the safety of the tested drug and on the clinical sensitivity and specificity of the diagnostic assay (19). This problem is exacerbated when multiple biomarkers and combinations of biomarkers enter the picture. Second, assigning only patients that are identified by the diagnostic test to the treatment group receiving the test drug leaves unanswered questions about patients who might have benefited from the drug, but went unidentified (31). Increasingly sophisticated patient characterization paints a future where small subgroups of the patient population benefit from available precision medicine, but the needs of individuals who fall in between are left unmet because of the narrow scope of clinical trials. Once again, systems or network approaches may offer answers to transform diagnostic tools and clinical development of precision medicine. Methods of systems biology build on iterative cycles between theoretical modeling and experimental testing of model-derived hypotheses. The model does not have to be mathematical. Important is only that it serves to describe the system being investigated and that it can generate hypotheses to be tested. Results from experiments, especially negative results, are then used to adapt and improve the model, thereby creating an ever-improving representation of the system with every cycle. Bielekova et al. (32) describe how this methodology can be incorporated into clinical trials. The idea would be to start with a systems-level model that incorporates all that is known to be relevant to the disease under investigation: pathways and their components, microenvironmental conditions that impact cellular behavior (e.g., tumor development), known interactions among pathway components, known interactions with potential drug leads, etc. This model is used in the design and optimization of one or more drugs and in the design of a multiplexed assay. This assay tests not only for biomarkers that forecast patient response to the developed drugs, but also for predictions that arise from the model, such as anticipated changes in pathway activity, or expected changes in cell morphology and metabolite profile. When patients enter clinical trials for the developed drugs, results from the multiplexed assay are analyzed in real time and used to assess the validity of the foundational model. A second testing cycle based on an improved model can take place in the

same clinical trial with an updated multiplexed assay. The I-SPY-2 TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2) is an innovative clinical trial that comes very close to this approach. A multiplexed profile of a patient’s breast tumor is made upon entering the trial. This profile is used to match the patient to one of several therapeutic agents being tested. The selected agent is the one that has demonstrated best efficacy for the patient’s tumor based on results from patients already enrolled in the trial. That is, efficacy data are processed upon collection and incorporated into a continuously optimized scheme for assigning new patients to treatment groups. Concurrently, results from the different treatments in the trial are compared to remove tested drugs that prove to be ineffective or toxic. The model also allows new drugs to be added to the trial as new treatment options.

In many ways, I-SPY-2 TRIAL is actually a collection of several trials unified by a shared infrastructure and by the integration of information derived from all treatments. Dr. Cui and Dr. Jørgensen see this model as a potential solution to overcome future challenges in precision medicine. As Dr. Jørgensen describes, “We will need to step away from the restrictions of controlled, randomized trials and develop more flexible structures, including open observational studies, that enable cooperation and data sharing. I can envision a knowledge-producing system that spans multiple clinical trials. Patient profile and trial outcome data must meet predetermined minimum requirements to be compiled in the system but can then be used to generate analyses comparing groups between and among different participating trials.” The shared framework leads to many advantages over conventional trial design supporting evaluation of a single drug. First, comparing the multiple treatments informs decisions earlier about whether a particular therapeutic agent should continue to be pursued. Second, the extensive profiles of patient tumors can provide data to support new indications for a drug if it demonstrates efficacy associated with a different set of biomarkers than originally planned. Third, data shared across several treatment groups can help overcome limitations due to small sample sizes arising from patient selection. Fourth, collected data are used to improve the disease models underlying the design and optimization of each therapeutic drug tested. The latter is a critical feedback loop to inform drug development with real and relevant clinical data, and to generate a more comprehensive and accurate picture of overall disease mechanisms.


Active precision medicine programs are uncovering potential hurdles and bottlenecks as developers, healthcare agencies and regulatory bodies rethink how and why drugs are created.

MOVING IN THE RIGHT DIRECTIONDr. Jørgensen has followed the development of precision medicine and recalls that, at the beginning of the 21st century, the underlying ideas were seen by many as provocative. “Now,” he says, “there is recognition of the value to patients, physicians, healthcare payors, and drug developers. A change in mentality has occurred…” Undoubtedly, replacing the “blockbuster” model with more precisely defined drug indications has benefited patients and reflects a genuine desire in the pharmaceutical industry to make better medicines.

The tendency toward smaller patient subgroups and the corresponding jeopardy to revenues has also not been lost on the pharmaceutical industry. With increasing frequency, measures are taken to expand the scope of drug development programs. Changes in direction of a program are, though serendipitous, evidence that workflows are retreating from the purely linear, stage-gate structures of last century and becoming flexible in their evaluation of a compound series. They engage a broader range of data sources to explain why a drug works or does not work, including external databases and internal knowledge management systems that break down the information silos characteristic of a strictly linear workflow (30). As a result, alternative indications or targets are commonly considered for a compound series. For example, the crizotinib development team had collected data on the inhibitory potency of the compound for 120 different kinases (18). This foresight and recognizing the relevance of the work by Soda et al. (17) enabled the program to shift focus and lead crizotinib down the successful EML4-ALK path.

Some drug developers are already shifting the focus of drug discovery to a target-agnostic approach by screening compounds in complex biological systems, such as primary human cells, stem cells, three-dimensional systems of cultured cells, and even zebrafish (33). Instead of measuring the activity of a specific molecular target, these phenotypic screening assays evaluate the effect of screened compounds on physiological responses of a system with few assumptions about the mechanism underlying compound effect. Thus, the approach allows developers to interrogate complete networks of molecules, to identify compounds that modulate

several molecules, and to uncover previously unknown modes of drug action. Drug leads identified in such screenings and insights from the measured physiological responses support target characterization and subsequent target-oriented lead optimization. What has proven difficult, however, is the development of assays that measure relevant effects and the translation of these measures into practicable structure-activity relationships to support lead optimization (33). Yet, as Dr. Köppen explains, with alternative methodologies such as “structure-readout relationships, where the readout is a set of relevant phenotypic parameters that reflect a meaningful manifestation of network function, [phenotypic screening] could be very powerful for lead optimization.”

Many developers also have the foresight to collect extensive characterization data on patients during clinical trials. These data support the approval of the tested drug, prompt new development programs, and as we have seen, are used to strategically place patients in the right treatment groups of a clinical trial. Changes to clinical development models are being discussed and explored. In addition to the I-SPY-2 TRIAL, so-called “basket” trials are a good example of the innovation that is emerging in clinical trial design. In basket trials, cancer patients are recruited based on the presence of relevant molecular aberrations and not cancer type (ie., histological classification). Thus, a drug is tested simultaneously for numerous cancer types, thereby expanding its scope of use (34). “In some ways this is a form of networking the development workflow,” explains Dr. Jørgensen, “because it examines multiple development pathways from the beginning rather than trying to repurpose a drug serially.”


Last but not least, the people behind precision medicine are evolving from traditional roles as chemists, biologists and pharmacologists to more interdisciplinary researchers with a perspective on all scientific areas that come into play for the development of a therapeutic drug. Dr. Cui explains that a medicinal chemist “must understand biology, must be a data scientist, must be familiar with the full spectrum of assays and analyses that inform his or her design work. A medicinal chemist must remain up to date on developments, always looking for new potential targets, new applications for a candidate drug and new ways of designing molecules to meet often contradictory requirements.”

One cannot deny that current precision medicine is paving the way into the future. Active precision medicine programs are highlighting the advantages of incorporating information about patients into drug development strategies, testing new clinical trial models and streamlined approval frameworks, and uncovering potential hurdles and bottlenecks as developers, healthcare agencies, and regulatory bodies rethink how and why drugs are created. Reflecting on the last two decades, Jørgensen describes the assimilation of precision medicine into the pharmaceutical industry as a sequence of steps that will ultimately lead to a new paradigm more closely resembling the mandate “the right drug for the right patient at the right time and in the right dose.” With a chuckle he adds, “there appears to be many more steps than I originally envisioned.” Progress has been slow, but Jørgensen points out, “it is important to remember that basic research is one thing, development another. Translating the first into the latter is resource and time-intensive.”

Precision medicine has ushered a turning point in cancer drug development that could mark the beginning of a real paradigm shift. This shift may not lead to the ideal of personalized medicine expressed 15 years ago, however, with an expanded perspective of how drugs interact with complex biological systems, precision medicine has the potential to generate better drugs for a range of therapeutic areas, from oncology to rheumatic, central nervous system, and metabolic diseases. And, who knows? Maybe “a drug designed for me” is just beyond the horizon. Only time will tell.

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WHITEPAPER No Drug Designed For Me - Elsevier · 2015-11-18 · No Drug Designed For Me. ... The term “personalized medicine” entered mainstream rhetoric enveloped in the hype