The Pharma Commercialisation Model of the Future


Amgen MEA’s Mohamed Nasser* examines the current pharma commercialisation model, posits how the entire healthcare ecosystem’s rapid and effective response to COVID-19 could be replicable in other disease areas, and makes his predictions for the industry’s future direction.


Probably more than at any other time in history, the last two years of COVID-19 have underscored the impact of the biopharmaceutical industry and the crucial importance of maintaining its economic viability. Paradoxically, and also partially due to COVID; intensified pricing pressures, fluctuating demand and a lack of adherence to medicine, and the increasing costs of R&D and digitalisation, among other factors, are challenging pharma’s P&L more than ever. This has led many companies to rethink their commercialisation model.

It may be worth looking at the current model before a new one evolves, especially as this model has stood the test of time with little modification – if any – for decades.

The current commercialisation approach can be seen through 2 dimensions:

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1. Disease priority: How high is the disease or condition on the healthcare system’s priority list?

Healthcare systems around the world do not have unlimited resources. No system can afford to manage all diseases with the same amount of attention and resources; hence prioritisation and sometime hard trade-offs need to take place.

Many factors drive that prioritisation. For example, most countries pay a lot of attention to diabetes and cardiovascular conditions, while others vary depending on specific demographics (e.g., Osteoporosis in the case of countries with a sizeable aging female population, or Hepatitis C where there is a high number of infected patients). It could also be seasonal or situational factors like asthma, flu, COVID, and Ebola etc. For good reason however, cancer usually stands out as a top priority, irrespective of healthcare approach or economics.

This priority list directly drives efforts to manage a given disease or fund its medicine.


2. Medicine innovation and differentiation: Let’s hypothetically place medicines into four categories:

Assume category “A” is for medicines that cure or treat previously uncurable or undruggable conditions e.g., the historic Introduction of penicillin, Sofosbuvir for hepatitis C, Luxturna for retinal dystrophy, and Insulin for diabetes.

Category “B” covers medicines that revolutionise the treatment of an already druggable condition e.g., Saquinavir (HAART) for treatment of HIV and its profound impact on survival and life expectancy, or oral PDE5 inhibitors that shifted erectile dysfunction treatment from awkward subcutaneous injections to a small effective, easy to administer, and tolerable tablet.

Category “C” are those medicines that introduce major benefits which are not exactly transformational but significantly improve disease management e.g., the introduction of SSRI vs. the less tolerable Tricyclic anti-depressant.

Category “D” are those medicines sometimes called “super-generics” or “Bio-Betters” which improve the patient experience and outcomes e.g., a medicine which was previously taken three times per day but which now can be taken once a week or monthly, or which has higher selectivity and affinity to the target bug with less irritating preservatives.

This categorisation is a simplification of tons of science, clinical trials, peer reviews, medicine protocols, regulatory approvals, cost effectiveness analysis, and pharmaceutical marketing. Many countries and payers have their own process of categorisation, sometimes simple like referencing another formulary, other times sophisticated and local. It wouldn’t be surprising if one argued that the above examples should be shifted up or down across categories based on their own perception or data, That’s somehow the essence of pharmaceutical marketing and competition.

It may appear mathematically logical that healthcare systems would embrace products in the green area or in Category A which treat high profile conditions like cancer and diabetes compared to those in Categories C or D treating conditions like headache. However, that is not necessarily the case.


Model in Action

In the chart and following examples, the disease prioritisation is hypothetical and all medicine names and characteristics are fictional for illustrative purposes only.

Two Hypothetical examples based on the chart above:

1) Consider two medicines launched at the same time: Diabetcure, an innovative product which cures diabetes and Fibrofree, which reduces fibromyalgia pain by 75 percent more than current medications. How would this look for the companies launching them and for the healthcare system?

2) Cardioease, a new product for the treatment of heart conditions which is equally effective but has remarkably fewer side effects than current options and significantly reduces the need for medical procedures, and Sclostable, which has recently been approved for the treatment of multiple sclerosis. It is marginally more effective and better tolerated than current medications but shifts treatment from inconvenient daily injections to a once-a-month injection. Again, how would companies manage those products launches and how would healthcare react to them?


Most companies actively and regularly screen for the healthcare priorities and unmet medical needs. Around 36-48 months pre-launch, they conduct detailed research focused on their targeted condition and how payers and key opinion leaders (medical influencers) will perceive the category of their to-be-launched product in treating such conditions. Is it an A, B, C, or D?

The disease priority assessment would determine the level of advocacy and PR, scientific and health economic data generation and other investments required to uplift the unmet need status to become a priority on the healthcare list.

Other aspects such as having an A-list celebrity supporting or suffering from the disease – such as Morgan Freeman and Lady Gaga for Fibromyalgia, Selena Gomes for Lupus, or Muhammed Ali and Michael J. Fox for Parkinson’s – certainly helps uplift the disease priority and awareness far more.

There are many successful examples of excellent disease prioritisation work combined with sophisticated scientific data generation and communication. Consider the landslide change of the state of the following diseases before and after the introduction of relevant medications:

  • Depression after SSRIs
  • Cholesterol after Statins
  • Erectile Dysfunction after PDE5 Inhibitors
  • Schizophrenia after Atypical antipsychotic
  • Rheumatoid arthritis after TNF inhibitors
  • Osteoporosis after Alendronate and SERMs

The list continues with millions of healthier people and saved lives primarily led by pharmaceutical investment not just in launching an innovative medicine but equally importantly, investing in uplifting the disease profile.

There are also examples of companies which have either gone too broad, too narrow, or too ambiguous and were thereby unable to pinpoint the unmet need. That confuses already busy healthcare professionals and the healthcare system at large, meaning that great science goes unused.


The Issue(s)

With few exceptions, healthcare systems and payers generally set new entrant medications as category “D” or less until proven otherwise, implementing price-focused policies more than broad cost effectiveness-based policies.

Another issue is that on one hand, most systems aren’t designed to compute the benefit of cutting the cost of procedures vs. the cost of Cardioease as in the earlier example, not to mention the societal benefit of saved working days vs. the cost of Fibrofree! On the other hand, many companies focus their R&D efforts on registration data to prove that their product is effective and safe. This depends on the level of their prelaunch readiness; upon announcing their price they realise how the healthcare system, media, advocacy groups, poor countries, and other stakeholders react to the price. Then, another journey of developing relevant data starts with more investment or eventually compromise on price directly or through transactional risk sharing agreements and alternative contracting that the system is not designed to accomodate. In some unfortunate cases, companies abruptly stop the product solely due to a lack of economic viability.


The Next Step

Pharma may need to think about some questions: Can they work collaboratively with healthcare systems? Can they organise the epidemiology, preclinical, R&D, EMR, patient evaluation and other rich underutilised data into meaningful knowledge that can bring benefits like helping assess the next product and improving healthcare prioritisation and spending? There needs to be a move towards data collaboration that goes beyond launch phase or adverse event collection to live ongoing data generation and periodic reviews to perfect patient outcomes and economics.

Interestingly enough, and despite the high stakes, the healthcare system and pharma performed exceptionally well in COVID-19 in rapid time and under extreme circumstance. This was phenomenal and saved many lives, but more excitingly, it is replicable and scalable to other diseases.

As those Zettabytes of data turns into knowledge, commercialisation may take a new turn. Maybe more AI infrastructure and operations will be needed instead of adding more marketers, access teams, and sales reps, but maybe not!


* This piece is solely my own opinion and does not represent my current or previous employers’ views

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