Axial - Matrix #8 (clinical trials)

Unique business models in life sciences

Axial partners with great inventors creating unique business models. Profiling exciting life sciences companies at the earliest stages is important. Rather than talk about their work specifically conveying the opportunity set is more important where the company and others in the field will bring to market currently confidential inventions to more people.

Clinical trials

Drug development is one of the few industries where a company is given a step-by-step guide (i.e. clinical trials) to get to a pile of cash likely well over $1B. Software has helped pre-clinical work but improving clinical results is still an outstanding problem. With $10Bs spent annually on clinical trials, the opportunity to bring new medicines to patients through better clinical trials is exciting with companies like Science37, PatientsLikeMe, Clara, Antidote, Unlearn, Health Gorilla, Curebase, and even PathAI building the toolkit and products to improve clinical work. Ultimately, finding the best patient population to test a new drug is an effective way to deal with the complexity of biology and reduce costs during clinical development - thereby increasing the efficiency of the funnel for making new drugs:

Image result for drug development funnel

Source: FDA


Types of clinical trials:

  1. Treatment trials test new treatments, new combinations of drugs, or new approaches to surgery

  2. Prevention trials look for better ways to prevent disease in people who have never had the ailment or to prevent a disease from returning (i.e. vaccines)

  3. Diagnostic trials to find better tests or procedures for diagnosing a disease/condition

  4. Screening trials test the best way to detect certain diseases/conditions

  5. Quality-of-life trials (supportive care trials) explore ways to improve quality of life for people with chronic illnesses

That are segmented in 4 phases:

  • Phase I: Researchers test a new drug or treatment in a small group of people (20-80) for the first time to evaluate safety, determine a safe dosage range, and identify side effects

  • Phase II: The studied drug or treatment is given to a larger group of people (100-300) to see if it is effective and to further evaluate its safety

  • Phase III: The studied drug or treatment is given to large groups of people (1,000-3,000) to confirm its effectiveness, monitor side effects, compare it to commonly used treatments and collect data that will allow the treatment to be used safely

  • Phase IV: After a drug or treatment is on the market, this phase is used to delineate additional information, including its risks, benefits, and optimal use

Source: Novartis

With the recent FDA shift toward post-market approval, already commonplace for medical devices, to therapeutics will bring medicines to patients sooner rather than later. Four recent developments ought to make this process move faster:

  • Master protocol studies - creating standards for trial design across indications; often generated by an independent group; has the potential to make trials plug-and-play where studies can be used in multiple contexts to validate a medicine

  • Synthetic control arms - simulating control arms; possible if the standard-of-care has been given to patients at a statistically significant scale

  • Basket trials - testing multiple drugs and combinations for multiple indications within the same cohort. Basket trials are being urged by FDA, but are very controversial since companies are resistant to share their molecules and put them in a trial where they may not have control. One pathway could be to use CROs to drive basket trials since they have built up the trust with biopharma.

  • Platform studies - share control studies between trials and reduce costs often by 50%

With these developments, an ignored but extremely valuable task is incentivizing the use of negative controls across each phase. Negative controls enable a company to go back in a trial and fix a problem that emerged. But due to capital constraints, money is often not spent on negative controls across an entire clinical trial.

With functional corruption in healthcare, therapeutics that improve patient outcomes will only accrue more value. The ability to improve clinical trials will only benefit from these drivers. Importantly, better clinical trial tools can help support drug development for diseases where recruitment and trial size is a limitation (i.e. cardiac disease). With the costs being so high and so much money and time spent, improving clinical trial design around recruitment, matching, and retention can lead to massive gains:

  • Patient recruitment (around 50% of trials are delayed due to patient recruitment issues)

  • Patient matching (patients historically are not precisely matched according to genome sequence, medical information, and behavior)

  • Patient retention (around 30% of patients successfully recruited fall away as the trial progresses)

With around 80% of trials delayed by at least one month where the potential losses are between $600K to $8M per day. Less than half of Phase II and Phase III studies complete enrollment within their original timelines, and it takes twice as long as planned for 39%. Eleven percent of research sites involved in such studies fail to enroll a single patient in active trials annually. With delays, a clinical trial still needs to pay for the cost of staffing, running the facilities, and the nature of the clinical procedure itself

Clinical trial companies can help increase trial participation. Moreover, by collecting more data precise matches between patients and new drugs can be made and retention can be achieved through a network effect. Due to biological complexity and inefficient patient segmentation, many drugs fail at Phase II/III - this is incredibly value destroying. The ability to not only get patients into a trial but match them with a drug that is likely to work can avoid these losses across a trial and improve the odds that a new medicine is approved.

Case studies


Antidote is focused on the recruitment problem. Their edge is using natural language processing to make it easier for patients to find suitable studies and by providing researchers with a tool intended to simplify the creation of study pages patients can understand. Over time, Antidote will benefit from the digitization of health-care. A key competitive advantage for Antidote is their high number of partnerships with drug companies: JDRF (a research organization for Type 1 diabetes), Lung Cancer Alliance, Eli Lilly, Novartis Pfizer, and Merck is an investor.


PatientsLikeMe has backing from iCarbonX. The product allows users to create an account and enter basic demographic information to get ongoing notifications about clinical trials they may qualify for. Once registered, patients can also connect with other patients who are working to manage the same condition, share health experiences and track treatment history. In the process, users generate data about the real-world nature of disease that help researchers, pharmaceutical companies, regulators, providers, and nonprofits develop more effective products, services and care. The company has over 325,000 members. By becoming a trusted source for real-world disease information, PatientsLikeMe is building a network that incentivizes users to contribute more of their health data.

Clara Health

Clara allows people to search for clinical trials. They are focused on a technical edge and are planning to build a network through hospitals. What allows them to partner with hospitals and create lock in? If they cannot build a network, Clara will have a hard time competing with Antidote and PatientsLikeMe unless they have an order of magnitude better technology.


Reduce cost and time to find total participants - the traditional model of recruitment is to find the right site and advertise for patients. Finding the right site is based on historical performance and sometimes it works and sometimes it does not Recruitment through advertisement can work, but it is expensive and because it is typically broad, it also tends to be slow. Often advertisements lead patients to poorly matched sites which results in a high number of false-positive screen failures. Patient recruitment in clinical trials is an area of growing challenge and concern for the pharmaceutical industry

Several factors have contributed to declining enrollment rates –  increasing volume of clinical trials conducted worldwide, increasing number of inclusion/exclusion criteria, the nature of modern medical practice (i.e. doctors lacking time, resources and staff to enroll patients). While current match-making solutions hold much promise for rectifying the problem of declining patient enrollment in clinical trials, centralized networks need to be created in order to facilitate an efficient experience for potential trial volunteers. In other words, if services are going to play a role in reversing recruitment shortfalls, they need to be networks of patient not just lists.

Clinical trial networks could learn from consumer models A consumer centric approach can be applied to clinical trials. The notion of patient centricity is becoming a significant strategic focus across all sectors of healthcare. When you need a clinical trial, the relevant context typically involves your physician. Perhaps matching services could involve the physician, offering a view for both the provider and the patient. Such a design would ensure that the patient gets the information they need while the provider plays the role of examining protocols and medical terminology

Using better models and data can help find patients, understand them, and connect them with clinical trials. The key is where they are and when they are looking. For example, Antidote has used their large number of partnerships to generate patient reach and engagement opportunities, which allows us them target, and re-engage patients as they match to new study criteria. The combination of scale and quality results in cost savings and efficiency.

Matching and retention

As a network scales, matching and engagement improves and the service begins to get significant patient and behavioral insights. Examples are the intent to participate given certain study criteria or what type of studies patients are matching to. These insights can help clinical trial sponsors with not only designing their trials upfront, but also amending their protocols during execution, as patient engagement data can flow back to design

A popular alternative is EHR feasibility analysis; however, this approach does not provide insights as to whether a patient is willing to participate in a study, or what factors veer a patient from not wanting to participating in a study. There are many different types of clinical trial matching services, but they all aim to connect the right patients with the right studies, aiding sites and promoting clinical research awareness. These clinical trial matching services can be categorized as: services that require registration and auto-match patients to clinical trials, services that do not require registration and simply allow patients to browse online databases of clinical trials, and disease-specific services.

Types of matching services:

  1. Registration-based services: patient seeking a clinical trial enter key demographic and health information into an interface. Based on this information, the service matches each patient with the most suitable studies currently enrolling, providing basic study information, as well as, typically allowing communication with other patients (i.e. PatientsLikeMe)

  1. Services that do not require registration: offer an easily searchable online database, allowing patients to search clinical trials that are recruiting patients and contact those that they are potentially interested in. Some of these services will allow you to contact them by phone, where a representative will speak with you about your condition and let you know about clinical trials that you may qualify for (i.e. Clara Health)

  1. Disease-specific services: specializing in certain diseases and conditions. Most of these are hosted by patient-advocacy groups that play matchmaker - linking patients to researchers who need them for clinical trials. These services usually allow patients to connect with and be a part of a larger community of people who are all focused and motivated to find effective treatments for the same condition. HIV/AIDS groups in the 1980’s started the trend of patient communities focusing on trial recruitment and retention, and dozens of disease-specific groups have followed suit - breast cancer, autism, diabetes, asthma, etc (ie. The Michael J. Fox Foundation for Parkinson’s Research).

Clinical trial network question checklist:

  1. What set of problems are you solving: recruitment, matching, retention?

  1. Where do you get data from: patients, hospitals, foundations, drug companies?

  1. What are the incentives to provide data?

  1. What is the payment structure? Who pays? Is it up front or coupled to an outcome?

  1. Has the product shown an increase in the levels of efficiency in any trials?

Ultimately, as patient segments get smaller and therapies become more targeted, new trial designs and endpoints (i.e. patient reported outcomes) will emerge. New developments such as population driven clinical trials, right-to-try, and n-of-1 trials are promising increase the funnel of drug development and solve key problems in recruitment, matching, and retention of new medicines with patients.

Have a nice day.