Expert Feedback on Pain Points and Evolution in The Precision Medicine Landscape

by Dr. Andrew Aijian and Colin Enderlein


With the rise of precision medicine, we have seen an increasingly multi-disciplinary approach applied to medical innovation, with therapeutic, diagnostic, and real-world evidence developers working to enable clinicians to provide the right drug to the right patient at the right time.

Despite great enthusiasm for the shared mission of precision medicine, numerous barriers exist to its development, progression through the clinic, and widespread adoption into practice. The opportunities and challenges to implement precision medicine vary among different stakeholders, in many instances asymmetrically, with some players experiencing more hurdles than others. Overcoming these barriers requires that stakeholders, companies, and innovators can effectively identify these problems and efficiently deploy resources towards creating solutions.

Our goal is to establish coherent and comprehensive framework of the current state of precision medicine, from development of basic components (diagnostics, drugs, protocols) through to data collection and analysis in the clinic. In laying out this framework, we also present a view that allows an understanding of the interconnectedness across activities and reveals a path for forward and reverse translation of the data that informs next generation precision medicine developments. Ultimately, we present this model as stimulus  to encourage an end-to-end view of this landscape and for considering new approaches to precision medicine.

As we drafted this framework, we noted key pathways and pain points  that  have been adopted in the first generation of precision medicine launches. To this end, we conducted primary research with a variety of precision medicine stakeholders to identify and characterize these pain points and unmet needs within the precision medicine landscape. Additionally, we evaluated multiple scenarios for how precision medicine advancements could shape the future of cancer care and how these advancements would both address current issues while also introducing new opportunities and challenges for future stakeholders to consider.


We deployed a targeted, U.S.-focused primary research campaign of various types of stakeholders throughout the precision medicine landscape, spanning experts in early research & development in pharma and diagnostics companies through to data collection driven by clinical stakeholders and ultimate data analysis by RWD aggregators.

Our strategy was to use a first principles, hypothesis-free approach to identify key pain-points in the precision oncology landscape and to characterize which stakeholders are implicated and what downstream consequences they might face. To clearly elucidate where the pain points exist within the precision medicine landscape and whom  they  affect, we first created a comprehensive map to outline the key processes, activities, and workflows for different groups of stakeholders in precision medicine, including the inputs, outputs, and interconnectedness across each of the stakeholder groups and key processes (Figure 1).

This precision medicine “subway map” was used as an idealized framework to pinpoint the processes and activities that stakeholders identify as pain points or areas of key unmet need, as well as identify areas of potential disruption in the future. As a static depiction, this is not meant to represent the real-time “loop backs” or restarts common in such dynamic ventures.

To ensure we captured balanced feedback from various perspectives within precision medicine, we conducted primary research with stakeholders across four main verticals:

  1. therapeutic (Tx) developers
  2. diagnostic (Dx) developers
  3. clinicians/patients, and
  4. real-world-data (RWD) / Health AI experts.

In total, we conducted 22 interviews lasting 45-60 minutes each.

Discussions with each interviewee were split between two topics:

  1. a focused dive into which points in the precision medicine landscape represented the greatest areas of pain / unmet need; and
  2. an open-ended discussion on scenarios that could drive or catalyze change and disruption in the precision medicine landscape over a 5-10 year horizon.


Each interview began with walking participants through the structure of the landscape map and confirming if there were fundamental disconnects between this visual and how they viewed the landscape. Interviewees were largely aligned with the landscape map, but provided feedback that led to the inclusion of the “physician education/training” node in the “Care Team Journey” pathway, as well as an expansion of the nodes within the drug and diagnostic development branches into which RWD/E feeds.

Interviewees also emphasized that, while the landscape map represented an idealized view of precision medicine, there are many other minor steps, sub-branches, and workflows that add to the overall complexity of the landscape and prevent the seemingly smooth transitions between the different branches. Following this discussion, interviewees were presented with the  open-ended  question:  “please  describe the biggest pain points you see in precision medicine today.” Subsequently, stakeholders were asked to specify, from their perspective, which particular nodes on the landscape map represent the most significant pain points and barriers to the advancement of precision medicine, and to weight them according to the degree of pain. Based on this feedback, we could both identify and quantitatively characterize the scale of these pain points and unmet needs.


The second half of stakeholder discussions focused on understanding how the landscape might evolve in the future and elucidating what drivers  would  enact these changes. Again, discussions began open ended with the following question: “What  are the key changes  you expect  to see in the precision medicine landscape over the  next 5-10 years?” Subsequent questions would be tailored to understand what would cause these changes, which stakeholders would be impacted, and what (if any) impact these changes would have on previously discussed pain-points.

We applied the primary research methodology of coding the qualitative responses into specified themes in order to quantify the extent to which different aspects of the precision medicine landscape were expected to change by stakeholder type. A list of these themes can be found in Table 1 along with the corresponding landscape verticals they match.

As a concluding question to characterize the potential implications of future developments for precision medicine stakeholders, we asked participants to answer the following: “What are some of the downstream consequences of the expected changes you outlined?”  From  this final line of questioning, we were able to code multiple scenarios that might begin with a single catalyzing event but could permeate to every branch of the precision oncology landscape over time.

Note: The content of this paper focuses on the discussion around key findings and supporting secondary research; direct quotes and underlying primary data can be accessed on the previously published DeciBio blog (see Link in References).


To visualize the points within the precision medicine landscape that represent the areas of greatest unmet need, we aggregated interviewee feedback and calculated which nodes in our precision  medicine  subway map were most frequently identified as key pain points. To achieve this, we calculated a normalized aggregate weight for each pain point based on the feedback provided by the interviewees. The result of this analysis is a heat-map overlay of our precision medicine subway map showing the relative frequency and significance of pain points identified by various precision medicine stakeholders (see Figures 2 and 3).

This analysis highlights the most critical pain-points throughout the precision medicine landscape and identifies opportunities for the development of new products / services / solutions that can solve critical stakeholder

needs and drive precision medicine forward. Based on our analysis of the interviewee feedback, we’ve identified the most critical unmet needs in precision medicine as:

  1. Preclinical disease models that better capture / reflect systems biology
  2. Solutions reducing barriers to clinical trial enrollment
  3. Purpose-built and standardized interfaces, systems, and ontologies for real-world data capture
  4. Efficient and effective methods and channels for clinician education and support
  5. Channels of communication and collaboration within and between different types of precision medicine stakeholders

Below we elaborate on these unmet needs including context provided by our interviewees.


Drug and diagnostic translational R&D represents one of the biggest collective pain points. Stakeholders across the precision medicine landscape outlined the need for better disease models and diagnostic tools that capture systems biology complexity. While many of the problems stem from antiquated preclinical models (i.e. cell culture, animal models), the need for advancements in proteomics and improved workflows for sample management are seen as additional hurdles.


  • Cancer research models are too simplistic – Interviewees cited that the lack of in-vitro models that reflect systems biology hinders translational R&D and impedes the rate of drug discovery. As outlined in previous research, while cancer cell lines and mouse models have played an instrumental role in early drug development, these tools do not capture cancer heterogeneity, which hinders translation to clinical applications (Kemp, 20181). From an analytical perspective, stakeholders indicate that gene expression  and multi-omics tools are a step in the right direction, but price and turnaround time are too high and data analysis is too intensive to support high-volume use necessary for dynamic, iterative testing of a systems model.
  • Advancements in proteomics have not kept pace with genomics – Despite significant improvements in DNA/ RNA analytical capabilities, measuring these analytes is still a step removed from understanding the ground truth of the cellular processes played out in proteomic interactions and activity.  While some proteomic methods do enable high-throughput, high-resolution analyses (interviewees highlighted aptamer-based methods), it is still not possible to interrogate the proteome at the level of sensitivity and breadth as we can for the genome. Tools capable of characterizing the abundance, sequence, and interactions of thousands of proteins in parallel would be a significant evolutionary step towards translating discoveries into the clinic.
  • Research sample acquisition and management is inefficient – Even major pharmaceutical and biotech companies have difficulty securing and managing research samples. Poorly sourced and annotated samples and the lack of solutions for efficiently storing and tracking internal sample inventories are considered significant pain points, which are only expected to be exacerbated by the increasing adoption of liquid biopsy samples in translational research. Liquid biopsies are often collected on multiple occasions over time, adding another variable (i.e., chronology)  to sample management; on the other hand, researchers can then execute longitudinal studies. Pharma interviewees noted that sample acquisition and management challenges can add weeks to months to research program timelines, which can be costly in the context of the overall lifecycle of a drug; therefore, parties should consider the balance of total sample cost vs collecting more valuable data during the study design stage.


The rising stakes and requirements for precision medicine clinical trials erect new barriers that exacerbate an already challenging patient recruitment problem. Even though strong motivation exists to enroll and execute new studies with a precision medicine focus, the current clinical trial infrastructure is not optimized to support precision medicine trials. Key hurdles include the increasing complexity of biomarkers and trial protocols as well as more entrenched issues that can broadly limit clinical research such as access to RWD and negative perceptions of trials by patients.


  • Trial technical requirements become increasingly complex – Increasingly complex biomarkers and trial protocols will raise the burden of clinical trial participation on patients, making enrollment more challenging. This compounds the fact that more complex biomarker and treatment strategies (e.g., rare / composite / multiplex markers and rational combination therapies) require more patients to reach data significance, increasing the time and cost of (already costly) trials. Stakeholders fear that longer and costlier trials may translate into increasingly costly therapies.
  • Entrenched barriers to trial enrollment – While multiple companies are focused on developing solutions to match patients to clinical trials based on biomarker data, our interviewees indicated that perhaps a bigger challenge is the geographic distance between a patient and the precision medicine trial sites for which he or she qualifies. Interviewed patient advocates suggest that patients have an overwhelmingly strong desire to be treated in their own communities and are generally willing to travel up to ~80 miles to participate in a clinical trial. For many precision medicine trials, particularly early- mid stage studies without a large network
    of sites, a significant physical / geographical disconnect exists between patients and the trial sites. Figure  4 illustrates this problem: in many parts of the country, a patient with an IDH1 mutation is unlikely to participate in trials driven by IDH1 status, due primarily to physical distance (the circles on the  map in Figure 4 correspond to all sites participating in trials exploring IDH1 as a biomarker, the size of the circle corresponds to the expected recruitment radius for that site). More generally,  data  suggest that 25% of cancer trials failed to enroll a sufficient number of patients, and 18% of trials closed with less than half of the target number of participants after 3 or more years (Fogel, 20182).

Enhanced decentralized clinical trial solutions (e.g., just-in-time site enrollment, remote clinical trials) were cited by multiple interviewees as critical needs to mitigate many of the challenges associated with precision medicine clinical trials. Organizations such as the Clinical Trials Transformation Initiative (CTTI) have published guidance on the use of mobile technologies in trials (CTTI Mobile Guidance3), and conducting decentralized trials (CTTI Decentralized Guidance4), but while these provide best practices and a clear set of unmet needs, scalable solutions are still largely fragmented across multiple commercial players, and few commercial solutions targeting decentralization of oncology trials exist.

A final, and perhaps the most critical barrier to trial enrollment is the awareness, education, and perception of clinical trials among patients. Many patients have a negative perception of clinical research and are wary of participating given historical ethical failures in clinical trials, mis-perceptions about the treatment options (i.e., many patients think a clinical trial means they have a 50% of receiving nothing more than a placebo), and general mistrust of pharmaceutical companies.


The current infrastructure supporting commercial and technological health IT are perceived as fundamentally incapable of capturing and integrating health information in a way that enables the full potential of RWD/E  to be realized. Notably, this issue stems from the fact that most health IT infrastructure (i.e., EMR systems) are designed to optimize billing rather than facilitate efficient, clear, interoperable, and standardized clinical information. Additionally, the realization of the potential value of RWD  has spurred the development of a marketplace for medical information and has introduced numerous competing commercial interests that further fragments the data landscape and limits accessibility to RWD.


  • IT Infrastructure not optimized for RWD – Several core bottlenecks exist that limit the efficiency and efficacy of RWD aggregation, integration, and Fundamentally, the current healthcare IT systems were originally designed to facilitate patient billing rather than support data integration and analysis for clinical or research applications. Additionally, data entry has become particularly burdensome for physicians who are not incentivized to enter medical data into various systems in an RWD-friendly way. This is exacerbated by the fact that patients may have their data collected by numerous different providers and spread across a different EMR at each stage of their clinical journey. Further evidence of this pain point was recently published in JAMA (Shah, 20195), where authors outline that captured EHR and claims data is sufficient to reconstruct only ~15% of trials based on available patient records, highlighting how much outcomes data is simply not recorded.
  • Competing Commercial Interests – In the midst of high-value partnerships to acquire access to patient data and samples, hospitals and networks are increasingly establishing ways to differentiate and monetize their data sets. This has led to increased complexity in accessing RWD

for research and translational  purposes while also creating fragmentation and inconsistency. This fragmentation creates barriers that make it difficult to acquire and clean data at a sufficient scale to use it to for use in drawing clinically meaningful insights. Multiple stakeholders interviewed bemoaned the fact that most efforts to deploy RWD in drug development today are for endpoints that create only incremental value in precision medicine; the ability to leverage RWD to ask grander questions of big data sets is limited by the availability of data of sufficient quality and quantity.


The inability of providers to keep pace with the clinical and financial resources necessary to support precision medicine poses risks for broad adoption. Better tools are needed to support clinician  decision-making,  notably to assist clinicians in staying up to date with the rapid changes relevant to oncology and to help cut through the marketing “noise” of the numerous and often competing precision medicine offerings to make the best, evidence-based decisions.


  • Need to “push” rapid oncology  change to clinicians – While resources do exist to help educate clinicians to the latest best practices and guidelines (i.e. ASCO, NCCN), these typically exist as passive tools rather than proactive educational efforts aimed at ensuring clinicians employ the latest recommendations. Community clinicians, in particular, who often treat patients with multiple different types of cancers, cannot stay up-to-date with the latest guidelines for all cancer types they treat (on average, each NCCN guidelines get updated 3-4 times per year). Additionally, clinicians attempting to implement precision medicine are often faced with questions for which no good answers exists (e.g., how to respond  to a variant of unknown significance in a druggable oncogene, whether to start chemo while waiting for biomarker results); such uncertainties drive many oncologists to eschew precision medicine approaches altogether. help educate clinicians to the latest best practices and guidelines (i.e. ASCO, NCCN), these typically exist as passive tools rather than proactive educational efforts aimed at ensuring clinicians employ the latest recommendations. Community clinicians, in particular, who often treat patients with multiple different types of cancers, cannot stay up-to-date with the latest guidelines for all cancer types they treat (on average, each NCCN guidelines get updated 3-4 times per year). Additionally, clinicians attempting to implement precision medicine are often faced with questions for which no good answers exists (e.g., how to respond  to a variant of unknown significance in a druggable oncogene, whether to start chemo while waiting for biomarker results); such uncertainties drive many oncologists to eschew precision medicine approaches altogether.
  • Test overcrowding creates confusion around potential offerings – An interesting catch-22 is that, while clinicians prefer active efforts to inform and educate themselves about precision medicine, they also indicate that constant bombardment with sales pitches and materials from diagnostic providers creates confusion about what options are actually clinically necessary, validated, and reimbursed. An independent, standardized benchmark or recommendation from guidelines about the different testing options is lacking. Sifting through this cacophony creates  a clear barrier as clinicians will not make treatment decisions unless they are adequately informed.


Significant organizational and communication siloes persist throughout the precision medicine ecosystem. These siloes create inefficiencies and friction that hinder advancement and adoption of precision medicine by complicating the ability to create meaningful collaborations, partnerships, and deals, ultimately slowing the flow of data back into R&D. A critical consequence of these siloes is a regular misalignment in incentives and timelines for a drug and companion diagnostic co-development and launch, as well as potential for misalignment between key clinical stakeholders, namely oncologists and pathologists, in how they view precision medicine.


  • Misalignment in innovator incentives and partnership coordination – Some stakeholders perceive precision medicine to be largely reactive rather than a proactive effort by drug-developers (in short, pursued by biopharma only when necessary). Evidence of this can be seen in the mismatch between development timelines, reimbursement potential, and ultimate sales/ distribution channels between therapy and diagnostic developers. Despite frequent acknowledgement of the need for close diagnostic and therapy coordination at early development, collaborations and data sharing are often not initiated until later stages, causing rushed decision making and timeline constraints that can lead to suboptimal technical and commercial readiness.
  • Misalignment between clinicians – Fundamentally, barriers and friction occur whenever there is handoff of patients between clinical settings (e.g., from primary to specialty care, from surgical center to an oncologist, etc.), resulting in valuable lost patient data at each step. Rarely does

a single clinician ever have the full picture of information gathered and  decisions that must be made over an oncology patient’s journey (i.e. oncologists aware of drug options, pathologists aware of test options), complicating the flow of decision making even within a single clinical setting. As previously noted, this issue is often compounded by the ‘information overload’ clinicians face, in which they have to sift through mountains of external information (e.g., guidelines, biopharma companies, diagnostic companies, payors), much of which is more than they are able to reasonably act on (Kuelper, 20186).



Through the process of coding and categorizing the primary changes expected across the precision medicine space by different stakeholders, several key findings became quickly apparent. First, expected changes mapped closely to previously identified pain-points and areas of greatest unmet need. Second, expected changes generally mapped to stakeholder knowledge areas (i.e. Dx specialists outlined the most changes to the Dx space), but over a 5-10 year horizon, stakeholders from all backgrounds could speak to overall changes that would impact the whole ecosystem.

It is important to note that while the data in Figure 5 outlines the regularity with which specific changes were mentioned, it does not directly quantify  the impact these changes could have on the landscape. As an example, even though “decreasing cost of diagnostics” was a common expected change, the less frequently mentioned “improvement to big data analytics” could be broadly transformational in ways we cannot yet predict.


Within each of the four precision medicine verticals in our landscape map (Dx, Tx, Clinical, RWD), there were several potentially transformational scenarios that repeatedly emerged; these are outlined in Table 2 along with corresponding subtopics that were seen as drivers to enacting these scenarios.


Broadly speaking, diagnostics improvements were the most cited change expected to be seen in the precision medicine space. Underpinning this expectation is that the expansion of screening / early detection, driven by large panel testing, will become part of routine care.


  • Screening to become routine, driving detection of earlier stage disease – Screening could drive an overall decrease in the metastatic cancer population and boost the use of localized / adjuvant treatments. While patient advocacy and beneficial reimbursement will play a crucial role in increasing screening, recent evidence from an LBx CRC study showed ~50% lack of adherence in matched stool sample testing, underpinning that test modality may play a key role in driving adherence (Zeidan, 20197). On the flipside, risk of overdiagnosis may become heightened depending on screening test specificity. Ultimately, survival benefits can take years to establish, and demonstrating the true value of a test over a patient’s lifetime is a process that generally lag behind technological advances (McDowell, 20188).
  • Improved diagnosis frontloads data for multiple Tx lines – The extensive biomarker analysis enabled by large panel testing would help reduce the need for patient re-biopsy if they failed a line of therapy. Large panels could prove essential, especially in selecting treatments for mutation-targeting therapies; such comprehensive screens amount to assessing ‘all shots on goal’ (Morrison, 20199), allowing for multiple treatment lines that could be informed from a single early test. A further anticipated benefit  would  be the acceleration of clinical trial enrollment, especially in studies based on low incidence biomarkers and rare diseases.
  • Algorithm refinements may become the basis of Dx development given large panel availability – As large panel tests/ WES become cost effective, future diagnostic development may be based on algorithm refinement rather than be dependent on development of novel kits and chemistry.


Similar to Dx improvements, the most expected developments in the therapeutics space are general improvements to treatments (i.e. improved cost, fewer side effects, more indications, new drug classes). A more granular view of key drivers towards improved first-generation therapies include an increasing number of tumor agnostic drugs / indications, and extended efficacy / survivorship.


  • The need for physician education rises amidst the increasing number of treatments and matched markers – Education burden in the face of new therapies (especially those with increasingly complex companion diagnostics) was  top of mind for many interview participants; some believe this will present opportunities for pathologist and genetic counselors to play increasingly pivotal roles as educators and tumor board stakeholders. In other proactive steps, Annals of Internal Medicine announced in 2019 a Precision Medicine series specifically intended to educate clinicians about sequencing and translating related insights into clinical practice (Chung, 201910). There were also accounts of some Onco-EMRs integrating the latest guidelines, reducing the need to sift through professional societies (ASCO, ESMO) and national recommendations (NCCN), to identify the latest in approved clinical steps.
  • Tumors to become classified by markers are approved on the basis of biomarker classification regardless of tumor origin, tumors themselves may increasingly be characterized by their underlying biology rather than tissue type. While similar opinions have been presented by Friends of Cancer Research (Alexander, 201911), it is still unclear over what timeframe this will realistically evolve over, and the fact that variable responses are often seen in tumors of varying origin but similar mutation profiles may further stimulate reclassification (Febbo, 201912). Shifts could occur in clinical trial design as more basket studies initiate for patients based on tumor molecular profiling. Large panel testing would greatly enable treatment  sequence  determination as more indications are approved based on specified biomarkers.
  • Precision medicine will expand in community settings – With increasing drug efficacy / survivorship, patient advocacy and the promotion of treatment education could greatly expand. In the face of longer-term survival and a greater menu of treatments available in community hospital settings, primary care physicians could see  a heightened role as long-term maintenance care coordinators and patients may be less tolerant to traveling long distances for routine treatment.


From the patient perspective, an increasing number of resources will be available at all stages of care, driving increased empowerment as awareness of available options becomes more widely known. Patients will increasingly expect and demand precision medicine treatments while also becoming more aware of the value presented by their personal and medical data, prompting an increase in patient driven data generation and increasing discussions around ownership.


  • An increasingly holistic medical approach will be expected, with improved  continuity of care – Disjointed care journeys will be decreasingly tolerated as  patients are transferred between care providers for cancer management. This will be compounded as patients become more engaged in two-way decision making with their physicians (James, 201813) via enhanced education and utilization of readily available touchpoints (i.e. patient portals, telemedicine) rather than the traditional top-down model. Additionally, improved efficacy and better adverse event profiles could drive patients to expect more holistic, long-term care offerings that allow them to resume a normal lifestyle as cancer is treated as an episodic rather than chronic illness.
  • Patient brand awareness becomes driver of product adoption – An educated patient population will be more aware of brand name tests and treatments, in many cases proactively searching for offerings that might pertain to their disease.
  • Physicians expected to field more questions – As patients increasingly inquire about specific treatment or diagnostic options, the burden will fall on physicians to determine if these are appropriate and necessary for care. Patients  being treated at earlier stages of diseases with greater efficacy will also be more conscious of adverse impacts on quality of life, adding additional scrutiny towards possible treatment regiments.


The rate of patient data accumulation will continue to accelerate as improved methods for linking multi-modal sources emerge. Central to this, EMR improvements to enable more structured inputs from various sources (i.e. pathology, radiology) and a greater expectation for value-based care are seen as core to shaping the evolving RWD space.


  • EMRs may focus on patient data / outcomes vs billing – In the midst of increased EMR datamining, the lack of data standardization and data-linking bottlenecks have become increasingly apparent. As EMR’s become a more important part of tracking RWD, platforms built around data tracking rather than patient billing could see broader adoption.
  • Patient data from wearables and lifestyle factored in – Self-reported / self-tracked patient data is expected to see an uptick in the coming years; platforms that aggregate these data and provide clinically meaningful insights will be a key step in this process.
  • New outcome measures / biomarkers emerge – Unstructured machine learning tools will be better equipped to retrospectively analyze outcomes data with matched patient samples to identify patient subgroups with better response. Additionally, markers to predict adverse events could also be identified, especially as patient self-reported outcomes become increasingly recorded in a manner conducive to analysis.

In a point made by Eric Topol during the Advances in Genome Biology and Technology (AGBT) Meeting 2019, “we have well-exceeded our ability as humans to deal with data” (LeMieux, 201914), underpinning the need for enhanced computing to advance this field. Dr. Topol’s discussions acknowledged that our currently available precision medicine offerings are not particularly precise nor accurate. In taking up Dr. Topol’s challenge, researchers will drive precision medicine projects by asking better informed questions that will generate more accurate and precise data and better disease models. AI driven analysis of patient data, powered with solid insight, data, and hypotheses, will lead to matching patient profiles to entirely new classes of markers  and treatments linked to outcomes otherwise unclassifiable solely by more traditional manual data analysis.


We set out on this project to develop a model framework that would provide a map or guided tour through the pathways of precision medicine, from development in the laboratory to data collection and analysis in the clinic. Of course, we recognize this is a static depiction, and take steps to describe the dynamic nature  of the process in our analysis.

Our method was, first, to show the model to a variety of stakeholders and solicit their feedback to modify and/or confirm the framework and its components; second, we asked interviewees to identify those components  that represent the most critical unmet needs in precision medicine. Five “pain” points were selected through this polling-and-weighting process for deeper analysis; interviewee replies are listed and analyzed in Section C. Pain Points: Findings & Discussion. Our analysis laid out in the framework suggests that there is no single, insurmountable barrier preventing the advance of precision medicine for patient care, but rather a collection of numerous small-to-moderate barriers.

Despite these pain points, stakeholders in precision medicine remain optimistic about the promise and trajectory of precision medicine. As powerful laboratory and analytic tools come online, we look hopefully to companies, researchers, clinicians, investors, regulators, and other precision medicine stakeholders to overcome these barriers over time and target innovative solutions directed to patients’ unmet needs.

Dr. Andrew Aijian is a Partner at DeciBio Consulting, where he leads market landscape assessment, product development, and strategy engagements for a wide variety of stakeholders in the precision medicine industry. Andrew’s areas of interest and expertise include oncology biomarkers and diagnostics, -omics technologies, pharmaceutical services, and business intelligence data analysis. At DeciBio, Andrew has led CDx-related market evaluation and strategy engagements for stakeholders across the CDx landscape, including diagnostic manufacturers, pharmaceutical services providers, translational research tools companies, and pharmaceutical manufactures. Andrew has also served as author and editor for DeciBio’s companion diagnostics and oncology diagnostics market research reports, and developed DeciBio’s Immuno-Oncology Biomarker Analysis Platform and associated database of I/O clinical trial biomarkers. Andrew earned his Ph.D. in Biomedical Engineering from UCLA, focusing on the development of microfluidic research tools for biomedical applications. He obtained his B.S. in Chemical and Biomolecular Engineering from the University of Notre Dame.

Colin Enderlein has been active in precision medicine innovation and CDx commercialization at multiple phases of the R&D value chain in both the public and private sectors. Following the completion of his graduate work at The Karolinska Institute, Colin worked with the business development team at Seattle Children’s Research Institute to establish industry partnerships with a focus on curing childhood diseases. From here, Colin joined the business development team  at NanoString, where activities focused on CDx partnering and execution, and technology due diligence. He is currently a Senior Associate with DeciBio Consulting, where he specializes in research related to I/O therapies and their associated biomarkers, spanning both the pharmaceutical and genomic tools markets.


Blog reference: DECIBIO INSIGHTS: Pain Points, Unmet Needs, and Disruption in Precision Medicine – Part 1,

    1. Kemp, 2018;
    2. Fogel, 2018:
    3. CTTI Mobile Guidance:
    4. CTTI Decentralized Guidance:
    5. Shah, 2019:
    6. Kuelper, 2018:
    7. Zeidan, 2019:
    8. McDowell, 2018:
    9. Morrison, 2019:
    10. Chung, 2019:
    11. Alexander, 2019:
    12. Febbo, 2019:
    13. James, 2018:
    14. LeMieux, 2019: