	Among the multiple items on the “to-do” list of new FDA Commissioner Robert Califf is to make clinical research more efficient and reliable to accelerate the development of safe and effective treatments for patients. Califf has long advocated for a “learning healthcare system” that taps electronic health records to facilitate clinical trial design and enrollment, and to provide ongoing information on the effects and side effects of therapies in real-world use. Now Califf has a ready platform to promote such strategies to further precision medicine and expedited approval of innovative medical products. 

	In a March appearance before a Senate Appropriations subcommittee to discuss FDA’s requested budget for fiscal year 2017, Califf noted the agency’s success in approving more innovative drugs for market, many facilitated by expedited review programs and patient engagement in product development. His 

 cited efforts to refine clinical trial design and statistical methods of analysis, and to utilize advances in genomics and information technology to gain “more rapid, less expensive and more reliable answers about medical products.” 

	Most of the hearing, though, focused on the many other FDA activities important to the legislators, from effective monitoring of the nation’s food supply to halting the lethal abuse of opioids. Other top priorities for FDA involve tobacco regulation, combating antibiotic resistance, reducing high-risk drug compounding, and developing medical countermeasures to Ebola and Zika. Califf promised to soon issue guidances and regulations to further biosimilar development and to better manage FDA’s IT infrastructure. He also will have to seek Congressional backing for multiple new user fee proposals now being finalized by FDA and industry task forces. 

	During his confirmation process, Califf pledged that he would not lower FDA’s standards in evaluating the safety and efficacy of drugs and medical devices in response to challenges from legislators who feared that his ties to pharma would bias him towards industry. And while he avoids discussing drug prices, he recognizes that FDA can promote drug access by bringing more generic drugs to market, and that good information about medical product risks and benefits can support those who make coverage decisions. 

	Califf’s expertise in biomedical research should help him tackle these and other difficult regulatory and policy issues, as seen in his activity as FDA deputy commissioner for the past year. At a December 2015 FDA workshop on enhancing the collection and assessment of clinical data on diverse patient subgroups, he described the challenges in managing trials to generate such data. He recently opened a meeting of FDA’s Science Board that was called to advise the agency on the development and regulation of medical treatments for pain, where new product research is important for lowering opioid abuse.

	A blog posted on FDA’s website in February cites progress in clarifying terms and definitions related to the development of biomarkers and other tools needed to advance biomedical research and inform clinical trials. And last October, the commissioner endorsed an FDA report on coordinating the review of combination products, a hot topic in the biomedical research community. Cancer advocates have been pressing for a new entity to coordinate the development and oversight of cancer therapies and diagnostics, and Califf has said he will establish such a center under the White House Cancer MoonShot initiative. 

	At a March Institute of Medicine (IOM) workshop on “Neuroscience Trials of the Future,” Califf described some of the difficulties and opportunities facing FDA and sponsors in achieving more effective clinical studies on treatments for nervous system disorders. In the “ideal world” of a learning healthcare system, Califf commented, sponsors and investigators would conduct concept studies that lead to informed clinical trials, and those results then would be used to write practice guidelines and to guide more “real-world” studies that, in turn, would provide more evidence and refine practice.

	Unfortunately, Califf sees the research community opting for larger, more complex clinical trials, with the result that costs are “going off the scale.” Investigators enroll fewer patients per site because “we’re making things more and more complex,” something that he hopes FDA can address. He suggested that trials stop running multiple blood tests and collecting rare, non-serious adverse events from all patients, which “costs a ton of money.” 

	It’s not necessarily FDA that seeks more data from larger studies, Califf observed, but sponsors that shy away from simplified studies-often to avoid greater uncertainty. The challenge for FDA, he said, is to develop study models that all parties “feel good about.” FDA can’t promise it will approve a new product if the researchers do things in a certain way, as there are “always surprises with medical products,” he commented. But the agency can assure, Califf said, that it won’t come back later and say it didn’t like that approach.

Articles

News

FDA's new commissioner advocates for a "learning healthcare system" and less-complex clinical trial models that tap efficiencies in study design and enrollment.

Califf: More Informative Clinical Research Key to Drug Development

	The European Medicines Agency (EMA) has published draft guidance on the evaluation of anti-cancer medicines in humans.

	The 39-page document, which was issued on March 15 and is open for feedback until September 15, covers all stages of clinical development and addresses the development of treatments for malignancies, including drug resistance modifiers or normal tissue protective compounds.

	The agency is concerned that although many anti-cancer compounds are being developed, companies have only obtained a marketing authorization for a relatively small percentage of them due to poor activity or evidence of a detrimental safety profile. Until non-clinical models with good predictive properties have been defined, this situation is unlikely to change, and the absence of such models represents the greatest hurdle for efficient drug development in the near future, EMA noted.

	A central aim of the document is to promote the development of molecule-specific preclinical models to assess and predict anticipated activity as well as safety, which tends to be a standard approach used by developers of targeted molecules. The validation and predictive reliability of these models is a complex, time-consuming, and specialist process that requires tumor immunologists.

	EMA has sought to classify compounds according to reasonable designs of exploratory studies. This applies to cytotoxic compounds where the toxicity and overall response rate (ORR) are thought to be suitable markers of activity in dose-finding studies, compared with non-cytotoxic compounds where ORR and/or toxicity may not serve this purpose.

	Importantly, intra-patient dose escalation in Phase 1 can allow more effective drug levels to be reached, provided no toxicity is seen in two dosing cycles, state the authors of the document. This might help smaller biotech companies, for example.

	“The requirements of the characterization of the safety profile have changed with the emergence of molecularly targeted agents, immunomodulating drugs, and other non-cytotoxic agents. These types of agents may have other types of toxicity and are often dosed differently to conventional chemotherapy,” they wrote. “The dose-finding process and concepts such as dose-limiting toxicity may, therefore, need to be addressed differently than for standard cytotoxic agents.”

	Moreover, cumulative incidences by toxicity grade are not sufficient to characterize the toxicity profile. The impact of an adverse drug reaction on the benefit-risk balance may, for instance, differ importantly depending on how the incidence, prevalence, and severity change with time on treatment, and on the possibility to alleviate the reaction by dose reduction.

	EMA is urging companies to submit overall survival data compatible with a trend towards favorable outcome to capture potential negative effects on the activity of next-line therapies and treatment-related deaths. This approach is likely to have consequences on interim analyses, other than for futility, and cross-over, which should be undertaken only when available survival data provide the information needed for a proper evaluation of benefits and risks, explained the authors.

	As well as defining the appropriate doses and schedules of a cancer drug, the EMA emphasize’s the importance of identifying a target population with optimized benefits and risks in the section about exploratory studies. Advice is also supplied about studies for combinations of drugs with minimal activity, as well as combinations of conventional cytotoxics.

	No precise definition is given for either “trend towards favorable effects on survival” or “reasonably excluding negative effects on overall survival,” but the authors explain that if a major increase in toxicity is foreseeable, it is recommended that confirmatory studies are undertaken with the aim of showing overall survival benefit. They acknowledge that improved safety without loss in efficacy may constitute tangible aims and the design of non-inferiority efficacy studies.

	The safety focus of the document has added relevance in the light of the recent events of Zydelig, the PI3K inhibitor made by Gilead Sciences. The drug is being investigated by EMA after reports of serious side effects-including multiple deaths-among patients in several studies testing Zydelig in newly diagnosed leukemia and lymphoma. Gilead halted those trials in March. 

	Earlier in March, EMA published long-awaited guidance on how to comply with its policy on publication of clinical data. The agency is moving toward the operational implementation of its proactive publication policy, which has launched a new era of transparency, said Noël Wathion, the agency’s deputy executive director.

	The guidance will ensure that companies are aware of what is expected of them and are ready for the publication of these critical data, he added. EMA wants to work with companies that are concerned by the first wave of publication (i.e., those for which the decision-making process has been finalized since the policy entered into force). EMA is organizing a webinar in the second quarter of 2016 to allow companies to ask any outstanding practical questions. This webinar will be a live broadcast and will be available for future reference on the EMA website.

The agency is concerned about the lack of non-clinical models with good predictive properties in the oncology space.

EMA Releases New Advice on Human Trials for Cancer Drugs

	Are pharmaceutical company Phase III clinical trials becoming more complex? The most broadly based database available, ClinicalTrials.gov, does not support the assertion that clinical trials have become more complex in study/protocol design or execution. Illustrative is the number of countries used in pharmaceutical company sponsored Phase III clinical studies. 

	The number of countries used in commercially sponsored Phase III trials has not changed in recent years. It is essential to stratify the results or otherwise the data appear to show that the number of countries per study has actually declined. The chart at right stratifies the studies by planned study duration: less than one year, 1 to 2 years and 3 or more years. This is important because the longer the planned study, the more likely the study may be opening sites in additional countries. When stratified this way, the data show practically no change over the years covered.

Trend doesn't seem to fall in line with the perception of an increasingly complex trial protocol climate.

Number of Countries in Phase III Studies Remains Steady

	It’s a long-running battle, and the fat lady still hasn’t sung, so it ain’t over yet. But Europe moved one step closer in March to quenching the conflagration over transparency and clinical trial data. A weighty document running to nearly a hundred pages appeared from the European Medicines Agency (EMA), aiming to squeeze much of the oxygen out of the debate that has been raging for years about how much information companies should disclose about their products and their trials of them. (view the report 

	The guidance for the publication of clinical data explains to everyone who wants to know just how the agency is going to operate its new system of mandatory publication of data, and just what requirements are to be imposed on industry on submission of clinical data for publication.

	The new policy entered has been in force since Jan. 1, 2015, in that it covers the clinical reports contained in all marketing-authorization applications submitted from then onwards, but the first actual reports to appear are-because of the time-lag in processing applications-currently likely to appear only from this coming September.

	One of the hot spots in the document is the guidance on how to anonymize clinical reports for publication, so as to prevent any re-identification of trial participants. Bowing to the inevitable, given the wide range of methods available, EMA recognizes that no one method can be imposed, and permits some freedom of approach. But it gives recommendations to companies on how to best balance data utility for researchers with a minimal risk of re-identification, and companies will need to provide a report explaining their approach. That report will in turn be reviewed and published by EMA.

	But the real heat will be emitted by the document’s approach to commercially confidential information (CCI). Fierce arguments over the very concept of CCI have put drug companies (and regulators) at odds with healthcare campaigners in Europe, many of whom flatly refuse to recognize the merits of any methodology for identification and redaction of what companies perceive as sensitive material. In a scorched earth approach, the most vocal campaigners reject any such rights, and argue that the only ownership of such data lies with the trial participants whose involvement has generated the data.

	EMA has trodden a middle path of reason on the issue. It knows how inflammable this discussion is, and how determined industry critics are to pursue the fullest access to information. So its guidance makes clear that we are now very much into open season for data hunters. 

	“The vast majority of the information contained in clinical reports is not considered CCI,” it says. But “the vast majority” is not the totality, and EMA goes on to spell out its exceptions. It defines CCI as “any information contained in the clinical reports submitted to EMA by the applicant which is not in the public domain or publicly available and where disclosure may undermine the legitimate economic interest of the applicant.” There are, it says, “limited circumstances in which clinical reports might contain CCI,” and the data classified as CCI may indeed be redacted (or, to put it plainly, lest this column should be accused of unjustified redaction, simply blacked out). 

	With masterful understatement, the guidance document remarks: “It is anticipated that the preparation and publication of the documents will raise some practical questions, such as on how to apply the aforementioned redaction principles, and on the presentation and justification of the proposed redactions.”

	Where redaction takes place, companies will need to provide justification to EMA. The guidance clarifies which type of data EMA would typically refuse as being CCI and how the redaction of such data will be handled. Its broad framework starts from the principle that it will not accept redaction of any information in the public domain or that has no innovative features. It will also frown upon attempts to redact quality, non-clinical and clinical data which it believes to be necessary for the understanding of the rest of the clinical report, thus making its disclosure a matter of public interest. And the agency helpfully supplies some real-life examples of attempts that have already been rejected to justify redaction of material in reports.

	So don’t waste any time with the following arguments: “Unpublished data-These study results have not been published in any peered-reviewed [sic] publication.” “Company confidential information-Disclosure of these elements will harm [the company]’s commercial interests because it may enable third-party access to business-critical information.” “This information can be interpreted out of context. Such interpretation could lead to a misleading image of the safety profile of the product.”

	Clinical reports will be published at the conclusion of regulatory decision-making in the centralized marketing authorization procedures-irrespective of whether the decision on a marketing authorization application is positive or not. The reports-with anonymization and agreed redaction-will be published by EMA on its corporate website, within 60 days of the final decision for marketing authorization applications, line extension applications, and extension of indication applications. Where an application is withdrawn, the publication of the redacted/anonymized clinical reports will take place within 150 days after the receipt of the withdrawal

	The hope within EMA is that its approach will damp down the debate and allow attention to return to the content of reports, rather than the processes for accessing the data they contain. The agency has been engaged in extensive consultation with all parties concerned throughout 2015, and is cautiously optimistic that its patient negotiation has permitted a well-balanced set of requirements to emerge that can satisfy all sides.

	Now that D-Day is approaching for publishing the first reports under this new dispensation, the agency is planning to hold talks with the companies at the front of the firing line-those for which the decision-making process has been finalized since the policy entered into force, and whose reports will constitute the first wave of publication. It is also planning a webinar in the late spring for companies to raise outstanding practical questions. 

	Further down the track, still more fiery exchanges can still be expected, because the scheme now starting to deliver real reports is only the first phase of the agency’s CT transparency bid. While this first phase deals only with publication of clinical reports, a second phase will deal with the still-more sensitive issue of publishing individual patient data. But this is still on the back burner; the agency says that although it is committed to moving in that direction, no dates have been set, and it “will be implemented at a later stage.”

	Ultimately, the success-or failure-of this attempt to calm spirits on access to trial data may depend more on emotion than on logic. There are implacable views among the most earnest advocates of access to data, who recognize no claims for exemption and who, in the style of Wikileaks, demand full release of everything all the time. A lengthy and carefully-reasoned list of guidelines on the right and wrong way of doing things may not prove a sufficient response to that type of argument.

Peter O'Donnell is a freelance journalist who specializes in European health affairs and is based in Brussels, Belgium

View from Brussels

New policy hopes to calm the fervor around clinical trial data disclosure and confidentiality issues.

Will EMA Rules Calm the Disclosure Debate?

	Clinical trials with at least one substantial protocol amendment require several hundred thousand dollars in unplanned direct costs to implement. But perhaps the most expensive impact is the unplanned incremental cycle time tacked on to the study. A new analysis by the Tufts Center for the

 Study of Drug Development (Tufts CSDD), in collaboration with more than a dozen sponsors and contract research organizations (CROs), indicates that protocols with at least one substantial amendment take an average of three unplanned months more to complete than do those without an amendment. These findings shed new light on the importance of adopting new strategies to reduce select amendments. 

	Between March and July 2015, Tufts CSDD and 15 pharmaceutical companies and CROs collected data from 836 Phase I – IIIb/IV protocols approved between 2010 and 2013. Protocols approved within the more recent 12-month period were excluded from the study, as these had the potential to continue accumulating amendments following the conclusion of clinical trial data collection. From the protocols reviewed, Tufts CSDD analyzed data from 984 amendments. Seven of the 15 participating companies also gathered direct cost data from 52 protocols for which substantial amendments had been identified during the January and May 2015 timeframe. This study was supported in part by an unrestricted grant from Medidata Solutions.

	Only substantial protocol amendments were evaluated in this study to ensure a more consistent assessment of prevalence and impact. Substantial amendments were defined as any change to a protocol on a global level requiring internal approval followed by approval from the institutional or ethical review board or regulatory authority. Country-specific amendments that affected protocol designs for clinical trials within a given region alone were excluded.

	The majority of protocols (57%) had at least one substantial amendment. On average, across all phases, the typical protocol had 2.1 substantial amendments. Phase II and III studies had the highest prevalence at 77% and 66% of the total, respectively.  The average number of substantial amendments per Phase II protocol was 2.2; Phase III studies-typically the longest duration and the costliest to conduct-had the highest mean number (2.3) of substantial amendments. 

	Sponsors report that the vast majority of changes made to an approved protocol originate internally. Only one-in-six (16%) stems from a regulatory agency request. The most common changes addressed by a substantial amendment are associated with modifications and revisions to study volunteer demographics and eligibility criteria (53%). Four-out-of-10 (38%) changes are related to modifications to safety assessment activity; 35% are related to typographical errors; 27% are associated with endpoint modifications.   

	In the Tufts CSDD study, sponsors and CROs reviewed their respective amendments and indicated the degree to which they could have been avoided. One-out-of-four substantial (23%) amendments were considered “completely avoidable” and 22% were considered “somewhat avoidable.” Avoidable amendments included protocol design flaws, errors and inconsistencies in the protocol narrative, and infeasible execution instructions and eligibility criteria. 

	Approximately one-third (30%) of substantial amendments were deemed “somewhat unavoidable” and 25% were classified as “completely unavoidable.”  The causes of unavoidable amendments included manufacturing changes, the availability of new safety data, changes in standard of care, and regulatory agency requests to change the protocol design. 

	The total median direct cost to implement a substantial protocol amendment for Phase II and III protocols was $141,000 and $535,000, respectively.

	No surprise-substantial protocol amendments significantly impact some study scope elements and the entire study conduct cycle. But the new Tufts CSDD study puts some real metrics on the table: Studies that had at least one substantial amendment saw a significantly higher reduction in the actual number of patients screened and enrolled relative to the original plan. This may have been due to sample size re-estimations and concrete steps taken to reduce patient screening and enrollment burden. In contrast, protocols that had no substantial amendments saw only a modest reduction in the actual number of patients screened relative to plan; and a modest 

 in the actual number of patients enrolled relative to the original plan.

	Substantial amendments significantly increased cycle times at individual time points and throughout the study duration, suggesting that the delays associated with amendment implementation are not recovered or reversed later in the study. Study initiation durations (i.e., protocol approved to first patient screened) were, on average, 18% longer for protocols with at least one substantial amendment compared to those without an amendment. This difference was not statistically significant, as expected, since the majority of substantial amendments are implemented once the study is underway.

	For those protocols with at least one substantial amendment, the time points from protocol approval to last patient last visit (LPLV) and from first patient first visit (FPFV) to LPLV were significantly longer-at 90 days and 85 days, respectively-compared with those protocols without an amendment. 

	A whopping 5.5-month increase in time was observed in the “first patient participation cycle” (i.e., from first FPFV to first patient last visit [FPLV]), suggesting that the implementation of substantial amendments impacts study volunteers differently depending on when they are randomized and enrolled in the clinical trial.

	A large and growing number of sponsors and CROs recognize the incredible unplanned and unbudgeted toll that protocol amendments take on study budgets and timelines, and the major opportunity to improve clinical trial efficiency and performance. Companies are routinely gathering metrics to monitor their protocol amendment experience. A number of sponsors and CROs are leveraging new technologies and implementing new mechanisms, functions, and processes to optimize protocol design.  

	Amgen, for example, has implemented a new Development Design Center to assist clinical teams in designing better studies before going to the protocol-authoring stage. The Center taps experts and data to facilitate decision-making and promote a deeper understanding of design-related trade-off decisions and their impact on executional feasibility.  

	Pfizer and GlaxoSmithKline have implemented extensive internal review processes to improve protocol quality and reduce amendments.  GSK implemented a new governance mechanism several years ago. Pfizer recently revised its standard operating procedures (SOPs) to require that all protocols go through a detailed protocol and amendment review prior to implementation. The first step in this process calls for a review by a senior-level governance committee to achieve consensus on the design elements of the study, to ensure that the protocol is consistent with the overall development plan, and to challenge the executional feasibility of the protocol.

	Eli Lilly has implemented three core initiatives throughout the organization to simplify and focus protocol design; to incorporate patient-centered approaches; and to streamline the drug development process. One approach to support these initiatives is to solicit input-before protocol approval-from patients and investigative site staff during a simulation of study execution and the participation experience. Lilly’s study teams observe these simulations to identify and address feasibility issues that could potentially trigger the need to amend the protocol.

	EMD Serono routinely conducts patient advisory boards to solicit patient feedback on protocol design and the feasibility of the schedule of assessments. These boards are conducted globally, each among six to 10 patients in collaboration with patient advocacy groups.

	Lastly, TransCelerate BioPharma has made protocol feasibility one of its top areas of focus in 2016. TransCelerate recently released a Common Protocol Template, offering a common structure and language to drive protocol design quality and identify areas of misalignment between protocol endpoints and their respective procedures. TranCelerate’s initiative is among several other common authoring templates now available, including one developed by a community of global medical writers-the SPIRIT initiative-and launched a number of years ago.

	Sponsors and CROs are rallying to reduce the number of avoidable amendments and ultimately improve protocol quality, executional feasibility, and efficiency. The anticipated improvements in study performance and cost could not come at a better time, given rapid growth in the scientific and executional complexity of protocol designs and growing interest in patient-centric drug development.

, MBA, is the Director of Sponsored Research at the Tufts CSDD and Chairman of CISCRP, both in Boston, MA, e-mail: 

Clinical Trial Insights

Study provides real metrics on the trial-scope affects of protocol changes-shedding fresh light on the importance of adopting new strategies to reduce select amendments.

Acknowledging Cycle Time Impact from Protocol Amendments

	Our health is a reflection of who we are and how we live. In an information age that allows freedom of choice and ubiquity of options, the rise of personalized care is inevitable. The promise of precision medicine not only offers a newfound science to treat life-threatening illnesses, but also realizes an ideal medical care approach, treating each person as a valued individual via pinpointed diagnostic assessments and optimized therapeutic interventions.

	Technology continues to shape the way people live, with on-demand access to immense information and multi-channel communication. The scientific boom of gene sequencing is amplified by our ability to correlate genotype with phenotype and behavior, enabling rapid advances in disease diagnosis and treatment. 

	A single, human, whole genome sequence often can generate 500 megabytes of raw data. It’s easy to see how this can quickly turn into petabytes of data with even a relatively small cohort of patients. Even then, more data will be added to the mix, as pathogenomics, proteomics, and metabolomics evolve. Researchers require high-performance tools to manage increasingly large, complex data sets to extract scientific intelligence from raw data.

	The White House Precision Medicine Initiative’s Cancer MoonShot program migrates from the realm of fantasy, to distinct possibility, and perhaps, to reality through a merger of information technology and genetic science. To improve the lives of people with cancer and other life-threatening diseases, a set of key technology elements will be necessary to ensure success:

		Collaboration between researchers and clinicians, aggregating data at the patient level in support of disease-oriented research cohorts.

		A high-performance, technology infrastructure to enable rapid, accurate analysis of large volumes of data.

		Structured data models to ensure consistency and reproducibility in results.

		Scalability to ensure that knowledge generated through research can be applied broadly across the clinical setting, while longitudinally, clinical data continues to feed the research environment.

	Ultimately, cancer therapy is only the beginning of the coming wave of the kinds of scientific advancements linked to genomics and accelerated by informatics. Every aspect of the human care spectrum offers areas of potential advancement, from birth and hereditary disorders, through wellness and pre-sickness, all the way to disease management. 

	Today, only about 38% of consumers have heard of precision medicine, have only shallow knowledge about it and do not associate it with genetic medicine.

In the end, the marker of success for precision medicine is that the term simply vanishes, and its principles become fundamental to modern medicine.

PMC Survey: U.S. Public Opinion About Personalized Medicine, 2014, 

http://www.personalizedmedicinecoalition.org/Userfiles/PMC-Corporate/file/us_public_opinion_personalized_medicine.slides.pdf

 is Senior Vice President and General Manager, Oracle Health Sciences 

A Closing Thought

For programs such as the Precision Medicine Initiative’s Cancer MoonShot to have a chance at success, following a set of key technology mandates will be critical.

The Promise of Precision Medicine

Historically, most medications given to children had not been evaluated in pediatric clinical trials due to logistical and ethical challenges. Indeed, while children represent about 40% of the world’s population, only 10% of the drugs on the market have been approved for pediatrics.

Without a proper and approved clinical process, physicians are left with potentially unsafe dosing and therapeutic approaches for children. The result is a continuation of the off-label prescribing.  

	To address this urgent medical need, both the U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) now require pediatric trial plans-the Pediatric Study Plan (PSP) and the Pediatric Investigation Plan (PIP), respectively-as part of the approval process for new drugs. The combination of the Best Pharmaceuticals for Children Act (BPCA) and the Pediatric Research Equity Act (PREA) and these new regulatory requirements are starting to move the pendulum towards safer, more effective medicines for children. During the five-year period between 2007 and 2013, 469 pediatric studies were completed under BPCA and PREA; by August 2014, 526 labeling changes were made.

 Similarly, in the European Union, around 300 products have had label changes approved for safety, efficacy, or dosing for pediatrics since 2007.

	While these requirements have spurred growth in pediatric clinical research, there are still major barriers to successful pediatric drug development. Almost half of the trials conducted in recent years have failed to demonstrate either safety or efficacy. A total of 44 products had failed pediatric drug development trials submitted to the FDA between 2007 and 2014.

 An analysis by Gilbert J. Burckart, PharmD, and his FDA colleagues revealed several common factors that contributed to the widespread failures: suboptimal dosing, differences between adult and pediatric disease processes, and problematic study designs. 

	In the cases where suboptimal dosing contributed to the failure to show efficacy, there were two frequent issues: not testing a range of doses, and limiting pediatric drug exposure to that which was shown to be efficacious in adults. Testing a range of doses is critical to understanding dose-response

 relationships for a drug. Also, if the disease process differs between children and adults, then matching the drug exposure to that observed in adults may not be effective, and ultimately result in clinical trial failure. 

	An understanding of pediatric disease-its natural progression-is crucial for selecting outcomes for clinical studies, including the primary efficacy endpoint, safety, and biomarkers. Finally, problematic study designs are a significant contributing factor in clinical trial failures. Some of these issues included lack of a control group, stratification, and inadequate assay sensitivity.

A biosimulation framework to support strategic decision-making

	First, it’s important to clarify some definitions regarding pediatric age groupings. According to the FDA guidance, neonates are from birth to one month, infants are from one month to less than two years of age, children are from two to 11 years old, and adolescents are from 12 to 18 years old. As pharmacokinetics (PK) and pharmacodynamics (PD) may change between each age range, drug developers may need to develop dosing regimens specific to each subgroup.

	The very nature of human growth and maturation makes the prediction of pharmacokinetics in children especially challenging. Drug disposition in children differs from that of adults in numerous ways. For example, the kinetics of drug absorption may be different in children versus adults due to changes in the expression of intestinal drug transporters and drug metabolizing enzymes during development.

 Likewise, drug distribution changes with age as neonates (birth up to one month) have much higher total body water compared to adults. Finally, organ maturation has a significant effect on drug metabolism and excretion. Children have relatively larger livers, lower glomerular filtration rates, and less renal tubular absorption and excretion compared to adults.

 This distinct physiology means that traditional approaches such as allometry risk greatly over or under predicting drug clearance in pediatric patients, especially those that are less than one year old.

	Because of the special needs of children as well as ethical concerns, there are significant differences in clinical trial protocols for children versus adults. The FDA guidance document discusses these issues at length.

 Some of the major issues in pediatric clinical trials include the following:

		The type of PK study that is possible is often different in adults and children. While rich sampling is often conducted in adults, a sparse sampling procedure is generally preferred for young children to minimize the number and volume of blood draws.

		When studying neonates, sponsors may need to consider gestational as well as postnatal age when determining covariates for a population PK study.

		The formulation of a drug may change between age groups. Young children generally cannot swallow pills and may require liquid formulations.

	How can pediatric drug developers satisfy regulatory requirements and maximize drug safety and effectiveness while minimizing children’s exposure to experimental medications? Biosimulation-also known as model-based drug development-includes both empirical “top down” PK/PD modeling and simulation as well as “bottom up” physiologically-based pharmacokinetic (PBPK) models. It leverages prior information from preclinical studies, adult trials, peer-reviewed literature, and pediatric studies of related indications or drug actions. The integration of patient physiology, drug actions, and trial characteristics in models enables sponsors to optimize dosing and trial design. Indeed, in a study of 11 well characterized drugs, PBPK models of virtual subjects (birth to 18 years of age) showed greater accuracy in predicting drug clearance than simple allometry, especially in children less than two years of age.

 The increased certainty in biosimulated outcomes can help sponsors to ensure informative pediatric trials are performed and will gain approvals based on a smaller number of pediatric patients.

Opportunities during drug development for applying modeling and simulation techniques

	As the benefits of biosimulation become increasingly clear, regulatory agencies are also advocating its use to improve the success rate of pediatric trials from current levels.10 Indeed, a 2014 draft guidance from the FDA states that “modeling and simulation using all of the information available should, therefore, be an integral part of all pediatric development programs.”

	At each stage of clinical development, there are specific trigger points and opportunities to apply modeling and simulation techniques to increase the likelihood of success. Submission of the PIP is required by the EMA by the end of Phase I clinical studies. Biosimulation methods should be used to support the dosing rationale stated in the PIP. Population PK and PBPK models based on Phase I data from adults are frequently used to develop a drug model that aids 

with pediatric dose selection. Population PK or PBPK models can predict drug exposure across a wide range of ages and weights as well as maturation and organ function. The predicted drug exposure in pediatric patients can then be compared against observed values in adult subjects in Phase I to confirm the models and optimize the safety of treatments. This approach can also be used to develop a sparse sampling strategy that optimizes the assessment of PK parameters while minimizing the number of blood draws and other invasive procedures. Pediatric PBPK and population PK models can be used synergistically during drug development. The former have recently been used to aid in the determination of optimal dose and sampling times for population PK.

 Conversely, the results from population PK models can be used to further optimize pediatric PBPK models.

	Another important use for PBPK models in pediatric drug development is evaluating the risk of drug-drug interactions (DDIs). DDIs are a primary threat to the safety and efficacy of clinical practice. Clinically-relevant drug interactions are primarily due to drug-induced alterations in the activity and quantity of metabolic enzymes and transporters. Indeed, DDIs that cause unmanageable, severe adverse effects have led to restrictions in clinical use, and even drug withdrawals from the market. 

	The magnitude of any DDI depends on the fractional importance of the inhibited metabolic pathway. The pattern of CYP metabolic enzymes that contribute to the elimination of a drug may not necessarily be the same in children compared to adults. Thus, it is difficult to use information about DDIs in adults to inform the likelihood of pediatric DDIs. And, again, there are practical and ethical problems with evaluating DDIs in pediatric clinical studies. A 2012 guidance from the EMA states that PBPK simulations may be used to predict the effects of drug interactions in multiple special populations, including young pediatric patients.

	Use of the Simcyp Pediatric Simulator to simulate DDIs revealed that in certain scenarios, neonates could be more sensitive to a DDI than adults while the opposite might be true in other scenarios involving different CYP enzymes.

 Pediatric PBPK models may help provide information about the risk and magnitude of potential DDIs where there are no existing clinical data.

	Pharmacometrics tools are also invaluable in supporting pediatric study plans. The PSP should be submitted to the FDA at the end of the Phase II meeting, following the availability of exposure-response data in adults. To provide guidance on the conduct of pediatric trials, the FDA has articulated a pediatric study decision tree.

 The degree of similarity of disease progression and drug response between adults and children determines which of three major pediatric studies should be undertaken: PK only, PK/PD, PK, or efficacy. Safety studies are required in all of these scenarios. 

	The regulatory path taken determines the strategy for optimizing dosing. In the case that PK studies alone are used, the sponsor should build a population PK model customized for size and maturation and perform dose simulations that will result in drug concentrations within the range of those observed in adults. Using the PK/PD approach means creating a population PK/PD model that is customized for size and maturation and performing dose simulations that will achieve a target concentration based on the PK/PD relationship. Finally, utilizing a PK and efficacy approach involves building a population PK model and an exposure-response model, and performing simulations to find a dose that will produce a drug concentration that results in an adequate response. 

	Phase III studies in adults are performed to determine whether there is statistically-significant evidence of clinical efficacy and safety for an investigational drug. At this point, the PIP and PSP should be updated to reflect any new insights. This is also the time to develop final pediatric protocols. Clinical trial simulations using Phase II results can be useful for evaluating probability of success in Phase III.

Two case studies showing successful use of biosimulation for pediatric drug development 

Learning from one indication to the next: Eculizumab for atypical hemolytic uremic syndrome

	In some cases, information gained developing a drug for one indication can be leveraged to inform its approval for a different indication. PNH (paroxysmal nocturnal hemoglobinuria) is a rare, progressive, and life-threatening disease. It is characterized by rampant destruction of red blood cells (hemolysis) and excessive blood clotting.

Likewise, aHUS (atypical hemolytic uremic syndrome) is an ultra-rare genetic disease that causes abnormal blood clots to form in small blood vessels throughout the body. The sequelae of aHUS include kidney failure, damage to other organs, and premature death. There were no FDA-approved treatments for this rare disease. 

	Both aHUS and PNH are caused by chronic, uncontrolled activation of the complement system. During activation of the complement system, the terminal protein C5 is cleaved to C5a and C5b. C5a and C5b have been implicated in causing the terminal complement-mediated events that are characteristic of both aHUS and PNH. Eculizumab is a humanized monoclonal antibody (mAb) that binds C5, thereby inhibiting its cleavage. In 2007, this mAb received approval for treatment of PNH based on evidence of effectiveness from clinical studies.

	To help the sponsor obtain accelerated approval of eculizumab for the treatment of aHUS in both adults and pediatric patients, Certara scientists leveraged previous knowledge gained during its development for PNH. Their starting point was a population PK model that had been previously constructed in adult patients with PNH.17 This model was customized and used to develop optimal dosing strategies for adult and pediatric aHUS patients. 

	Comparing the case of adults with PNH to pediatric aHUS, it became apparent that children have a different response to intervention and that a different endpoint should be used. The PK/PD relationship in PNH was leveraged to measure the drug’s exposure and inform pediatric dosing for aHUS. Knowledge about eculizumab’s mechanism of action for PNH also suggested that optimal binding to the pharmacological target (C5) should translate into a clinical benefit.

	Identification of the therapeutic dosing window for a mAb in pediatric patients with a rare disease involved several steps. First, to ensure patient safety, the upper exposure limit needed to be determined. As a safeguard against toxicity, the upper exposure limit was capped at what had been previously observed in adults. To ensure efficacy, the minimum drug exposure also had to be determined. Using the predicted concentration of the soluble target and the binding characteristics of the mAb to its target, a minimum concentration threshold was set to obtain close to full inhibition of the target. Then, trial simulations using a population PK model were performed to determine which doses would optimize the probability of obtaining the mAb within the window of target engagement. This enabled the dosing recommendations to be determined for pediatric patients of varying weights.

	The clinical program for aHUS involved two Phase II studies and a retrospective observational study. A total of 57 patients with aHUS participated in these studies (35 adult, 22 pediatric patients). Two different biomarkers were used to assess the efficacy of treatment. The proximal biomarker, free C5, showed complete suppression upon treatment with the mAb. Likewise, the mAb caused full inhibition of hemolytic activity (the distal biomarker).

 The primary endpoint indicated that the response to the intervention exceeded 95%. Patients treated with the mAb experienced several benefits including higher improvement in platelet counts and other blood parameters and better kidney function, even eliminating the requirement for dialysis in some patients. Soliris® (eculizumab) received FDA approval to treat aHUS patients in 2011.

Using PBPK modeling to assess differing drug formulations for pediatric patients

	Quetiapine is an atypical antipsychotic drug for the treatment of schizophrenia, bipolar disorder, major depressive disorder, and generalized anxiety disorder. An immediate release (IR) formulation of quetiapine was first approved by the FDA in 1997 and has been extensively studied in adults, children, and adolescents. Regulatory approval for the extended release (XR) formulation was granted for use in adults, with the requirement that pediatric studies must be carried out for children over the age of 12.

	Various factors influence the bioavailability of different formulations including the release of the active ingredient, its dissolution and permeability across the GI 

tract, as well as intestinal metabolism. Furthermore, alterations in PK in children can be due to differences in absorption and transit rate, organ size, blood flow, tissue composition, and metabolic capacity at various developmental stages. The challenge was to integrate the available 

 ADME, physiochemical, and clinical data into PBPK models to predict the effects of age and formulation on the PK of quetiapine in young subjects.

	Scientists at Certara and AstraZeneca developed PBPK models that predicted, with reasonable accuracy, the effects of CYP3A4 inhibition and induction on the PK of quetiapine, the PK profile of quetiapine IR in both children and adults, and the PK profile of quetiapine XR in adults. These validated models were then used to simulate relative exposure following XR formulation in adolescents (age 13-17) and children (age 10-12). In both groups, the predicted exposure to quetiapine XR followed a similar pattern to the IR formulation, with 300mg XR once-daily being comparable with 150mg IR twice-a-day.

	The high rate of trial failures, increasing regulatory demands, and ethical imperatives all require a reexamination of the current approach to pediatric drug development. Biosimulation is a proven approach that will help optimize trial design and inform the drug label. This approach can support global regulatory strategies that increase the likelihood of success for pediatric drug development programs. 

, PhD, FCP, is a Vice President and Lead Scientist; 

, PhD, is the Manager of Scientific Communications; all with Certara 

	1. Milne CP and Bruss JB. The economics of pediatric formulation development for off-patent drugs. 

	2.  Ito, S. Children: Are we doing enough? Clinical Pharmacology and Therapeutics. 2015. doi: 10.1002/cpt.167. [E-pub ahead of print]

	3. Momper JD, Mulugeta Y, Burckart GJ. Failed pediatric drug development trials.

. 2015. doi: 10.1002/cpt.142. [Epub ahead of print]

	4. Yaffe SJ and Aranda JV. (2010). Neonatal and pediatric pharmacology: Therapeutic Principles in Practice, 4th edition. Philadelphia, PA: Lippincott Williams & Wilkins.

	5. U.S. Food and Drug Administration, “Guidance for Industry: General Clinical Pharmacology Considerations for Pediatric Studies for Drugs and Biological Products,” December 2014, 

http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm425885.pdf

	6. Barrett JS, Della Casa Alberighi O, Läer S, Meibohm B. Physiologically-based pharmacokinetic (PBPK) modeling in children. 

	7. Edginton AN, Schmitt W, Voith B, Willmann S. A mechanistic approach for the scaling of clearance in children.

	8. Johnson TN, Rostami-Hodjegan A, Tucker GT. Prediction of the clearance of 11 drugs and associated variability in neonates, infants and children. 

	9. Maharaj AR and Edginton AN. Physiologically based pharmacokinetic modeling and simulation in pediatric drug development. 

CPT: Pharmacometrics and System Pharmacology

	10. Gobburu, J. (2010, March). How to Double Success Rate of Pediatric Trials? Presented at the meeting of the American Society of Clinical Pharmacology and Therapeutics, Atlanta, GA.

	11. Thai HT, Mazuir F, Cartot-Cotton S, Veyrat-Follet C. Optimizing pharmacokinetic bridging studies in paediatric oncology using physiologically-based pharmacokinetic modelling: application to docetaxel. 

. 2015. Accepted manuscript DOI 10.1111/bcp.12702.

	12. European Medicines Agency, “Guidelines on the Investigation of Drug Interactions,” June 2012, http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2012/07/WC500129606.pdf

	13. Salem F, Johnson TN, Barter ZE, Leeder JS, Rostami-Hodjegan, A. Age-related Changes in Fractional Elimination Pathways for Drugs: Assessing the Impact of Variable Ontogeny on Metabolic Drug–Drug Interactions. 

	14. U.S. Food and Drug Administration, “Guidance for Industry: Exposure-response Relationships – Study Design, Data Analysis, and Regulatory Applications,” April 2003, http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm072109.pdf

	15. Lathia C, Kassir N, Mouksassi MS, Jayaraman B, Marier JF, Bedrosian CL. Modeling and Simulations of Eculizumab in Paroxysmal Nocturnal Hemoglobinuria (PNH) and Atypical Hemolytic Uremic Syndrome (aHUS) Patients: Learning From One Indication to the Next. 

	16. U.S. Food and Drug Administration. (2007) FDA Approves First-of-its-Kind Drug to Treat Rare Blood Disorder [Press release]. Retrieved from http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/2007/ucm108869.htm

	17. Lathia C, Kassir N, Mouksassi MS, Jayaraman B, Marier JF, Bedrosian CL. Population PK/PD Modeling of Eculizumab and Free Complement Component Protein C5 in Pediatric and Adult Patients with Atypical Hemolytic Uremic Syndrome (aHUS). 

	18. U.S. Food and Drug Administration. (2011) FDA approves Soliris for rare pediatric blood disorder [Press release]. Retrieved from http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm272990.htm

	19. Johnson TN, Zhou D, Bui KH. Development of physiologically-based pharmacokinetic model to evaluate the relative systemic exposure to quetiapine after administration of IR and XR formulations to adults, children and adolescents. 

Feature Article

Pediatric trials now feature increased modeling and analytics for safer drug dosing and response.

Learning from Failure, Leveraging Biosimulation for Pediatric Drug Development Success

Until recently, the number often quoted as the cost of bringing a new drug to market was US $1 billion.

In November 2014, the Tufts Center for the Study of Drug Development reported that developing a new prescription medicine that gains marketing approval, a process often lasting longer than a decade, is estimated to cost $2.558 billion.

 Recent analysis shows that not only have costs risen, but there is high variability among companies in their “costs-per-successful-product” reaching the market.

 Improvements in product selection, product development, and investment decision-making would all improve the likelihood of a product’s successful market entry.

	The key issue facing the industry can be described as development productivity. On a portfolio level, this can be defined as a ratio of the current projects in the pipeline (work in progress or WIP), the probability of technical success (p(ts)), and the value of the pipeline (V) divided by the cycle time (CT) and the cost to deliver the pipeline (C).

	This equation conveys the balance of risk, time, and cost (with a factor included for numbers of compounds in a portfolio, though the equation is equally relevant for a single compound [i.e., WIP=1]), weighted against the probability of its technical success and its potential future value; each compound has its own set of dynamics related to the size of the population, market share/competitive landscape, unmet need, differentiation, and market access/pricing considerations.

	Biopharmaceutical companies have attempted to address the productivity issue by increasing the number of compounds entering a portfolio (“shots on goal”) and doing whatever they can do to decrease costs and cycle time. However, the productivity equation is dominated by the very low industry-wide “probability of success,” which, in the most recent data, is still only about 15% of new medical products entering human trials.

	Companies have tried to change these odds by targeting patient populations who are most likely to respond to therapy (e.g., through use of biomarkers); the goal is to try to increase the likelihood of positive efficacy results and corresponding positive pricing and reimbursement decisions. Even if a compound meets the regulatory standards that allow for its successful registration, there is no guarantee that the product will be accepted by boards/formularies responsible for pricing and reimbursement, making successful commercialization and patient access challenging. “Effectiveness” has essentially been added as the fourth hurdle to safety, efficacy, and manufacturing quality. Market access strategies continue to be shaped by influential stakeholders.

	There are a number of recent examples that speak to this point: England’s National Institute for Health and Care Excellence (NICE) did not recommend GlaxoSmithKline’s belimumab for the treatment of active lupus erythematosus.

NICE concluded that there was insufficient evidence of improved efficacy versus standard of care and did not recommend use, even though it was the first new approved drug in this indication for decades. In Germany, the law governing pharmaceuticals (AMNOG) was amended in 2011, introducing a formal Health Technology Assessment (HTA). Following this change, Boehringer Ingelheim decided not to launch the new oral anti-diabetic compound linagliptin (Trajenta®). Under the new law, the comparator was not the agreed comparator and the submission was assessed as not adequately justified.

	These examples illustrate that only focusing on development costs and cycle times is not sufficient and needs to be balanced continuously with the potential for a product’s reimbursement and commercial viability to ensure an adequate return on investment for new therapies. This requires forward-looking (and likely, disruptive) thinking at the earliest stages of development. Bringing unmet medical need, differentiation, and value-based thinking into the product development cycle in a way that is easily manifest and transparently addressable for both product development teams and decision-making stakeholders is essential in this approach. Integrating evidence-based planning and real-world evidence (RWE) has the potential to reap even bigger rewards for development productivity, as shown in Figure 1.

 To achieve this, we propose the Three-Pillar approach outlined in this paper.

Figure 1: Value capture from real-world evidence across the lifecycle for a top 10 biopharmaceutical company

Enhance probability of technical success 

	If biopharmaceutical companies are to realize the next level of transformation and achieve greater development productivity, they need to address the development cycle itself by integrating health outcomes, and using better, evidence-based decision-making approaches. As shown in Figure 2, the net present value of a product is highly dependent on development risk, costs, and cycle time. 

Figure 2: The relationship between development risk, cost, cycle time, and net present v

Net present value (green) is highly dependent on development risk (blue), development costs (red), and development cycle time (shown as time points: CDN 

 candidate selection; POC – proof of concept; DFL – development for launch - ; BLA/NDA biologics or new drug approval; launch). 

	Not surprisingly, including commercial viability and market access in the value equation when addressing development risk and cost early provides a far more complete picture for sound development decision-making. To achieve the desired outcome of better development productivity and commercial success, we propose a Three-Pillar approach based on identification of evidence needed for successful market entry and selection of the right plan to generate this evidence. These three pillars are illustrated in Figure 3.

Figure 3: Three-pillar approach linking clinical science and clinical operations underpinned by access to data, information, and knowledge

	In Pillar 1, a question-based process identifies what success looks like for the patient, physician, provider, payer, and regulators. A robust target product profile (TPP) is built from the answers to these questions; this will guide creation of an integrated evidence plan that incorporates the clinical and value evidence requirements to support the TPP. Refinement of product needs continues throughout the product life-cycle, including the design of late-stage development and post-marketing programs. This will result in intermittent, but iterative reassessments of the TPP and, correspondingly, the required evidence generation that such a reassessment will necessitate.

	Second, an integrated evidence plan (IEP) is designed. The goal is to create a direct line of sight from the TPP to the development strategy and straight through to the trials/studies in the program. The IEP divides required information into two categories: (i) data already available, and (ii) evidence that needs to be generated to advance stakeholder decision-making. The IEP then defines how the evidence will be generated within each clinical trial and real-world observational study, and how this will be leveraged to satisfy patient, physician, provider, payer, and regulatory requirements as defined by the TPP. When value-proving outcomes are investigated early in development, they validate the benefit statements and secure a positive recommendation from HTA and regulatory authorities. The IEP also allows a team to set futility criteria, so that if value evidence is not realized in a timely manner, informed and effective decisions to terminate the program can be made.

	In Pillar 3, scenario development and trade-off analysis are used to challenge assumptions both scientifically and operationally and create an evidence-based “level playing field.” This can be done most effectively through facilitated workshops where collective expertise (subject matter experts) and various options for generating needed evidence are reviewed, modified, and critically evaluated. Advanced analytics optimizes the evaluation of complex time/cost/risk/value scenarios in a transparent way to drive the decision-making process for key stakeholders.

	Productivity will benefit most when the approach to the pillars is taken in the context of an integrated partnership of the key stakeholders, with early modeling, visualization, and agreement on the “end game.” True end-to-end integration leverages business processes aligned with the three pillars and also leverages good information technology. Using innovative design approaches, timely access to real-world data (RWD) and patient insights can further drive positive results. This is especially true if the entire endeavor is focused on increasing access to more affordable innovative medical solutions that are not only commercially viable, but also deliver better health outcomes for patients.

	Rethinking the development model within today’s healthcare model requires companies to successfully apply the principle of “designing with the end in mind.” This means the starting point and the first pillar in our approach is a robust TPP, based on value to the patient, physician, and provider while meeting payers’ and regulators’ expectations (Pillar 1). Examples of key issues that might be addressed on a question-driven basis during this phase might include: defining the unmet medical need, key points of competitive considerations (versus the existing or emerging standard of care), key scientific claims required for registration, and early market access issues.

	These can be further refined to include the benefit of the treatment to the patient, how this benefit might be assessed, how the medicine will be differentiated in the market, what will drive physicians to prescribe the therapy, what would a payer require to increase or decrease access, and any likely evolution of regulatory requirements during the time-course of development. This forward-looking thinking is essential, since given the usual time-course of product development, it can be nearly a decade from the time of original decisions until a product reaches the market.

	Focusing on these issues upfront is essential to success of the Three-Pillar approach; the issues defined in the TPP will determine what evidence is required to support the program and ultimately, what results in terms of cost, time, risk, and value of the product. This closely knit interplay is shown in Figure 4. Furthermore, a value-based TPP defines the threshold that must be achieved for the product to be commercially viable. This then can be used to structure more formal “go/no-go” decisions that can help frame decision-making strategy and help to control biases that tend to favor continuing product development even in the face of low probabilities of success both scientifically and commercially.

How to integrate evidence-based planning and real-world evidence to boost clinical trial productivity.

Applied Clinical Trials-04-01-2016

Califf: More Informative Clinical Research Key to Drug Development

EMA Releases New Advice on Human Trials for Cancer Drugs

Number of Countries in Phase III Studies Remains Steady

Will EMA Rules Calm the Disclosure Debate?

Acknowledging Cycle Time Impact from Protocol Amendments

The Promise of Precision Medicine

Learning from Failure, Leveraging Biosimulation for Pediatric Drug Development Success

Value-Based Planning & Drug Development Productivity

Overcoming Early Phase Oncology Challenges

Imagining the Impossible: Immunity to Cancer

Applied Clinical Trials, April/May 2016 Issue (PDF)