3 Ways the Pharma & MedTech industries can accelerate Market Access
What are some of the key areas of improvement which can enable pharma and life sciences to achieve market access better and faster?
1. Making the best use of scientific advice from HTA agencies
HTA agencies require certain standards of evidence in order for innovative therapies to achieve their recommendation. Not every HTA agency has the same requirements, however, and the variance is a constant hurdle that pharma and life science companies need to overcome more quickly to get their products to the market faster and with a better price.
While most companies are aware that dialoguing with HTA bodies as early as possible, even in phase II, is key, the Centre for Innovation in Regulatory Science (CIRS) has prepared a briefing which expands upon this central advice.
CIRS recommended pharma companies to take advantage of multiple agency and regulatory meetings such as EUnetHTA’s “Early Dialogues”, “Shaping European Early Dialogues” (SEED), or also EMA regulatory & HTA formats. This collaboration has been proven to allow a better and earlier alignment of endpoints, comparators, and other needs for a specific development programme.
After the initial meetings with HTA bodies, pharma companies should continue dialogue to adjust the product development process better with the arising evidence. Ongoing dialogue with HTA agencies can be improved with informed feedback from pharma companies about the reasons for incorporating some advice, but not other. In return, agencies should prepare documents that define the key points that pharma companies need to know for successful evidence generation.
Internally, pharma companies need to maximize scientific advice better. HTA scientific advice is becoming an increasingly valuable resource to form a database of critical guidelines for improved decision-making. Pharma companies also need to be organized internally to disseminate this advice and implement it more effectively.
Use Case
In 2013-2014, Sanofi tested the SEED project collaborating with the EMA and 14 different HTA bodies. Development programs for 2 chronic diseases, a rare disease, and a cancer disease were discussed. While not achieving complete consensus among stakeholders, Sanofi reported a high satisfaction from the advice received about achieving outcomes, comparators, trial design, RWE, and economic modelling.
2. Improving multi-criteria decision analysis (MCDA) models for assessing benefits and risk to support better decision-making
Since 1999 NICE has been using the incremental cost-effectiveness ratio (ICER) per quality-adjusted life year (QALY) as the principal method for assessing the cost, benefits, and relative value of different medicines.
The QALY is a tool which quantifies both the added life expectancy and the quality of life granted by a specific innovative therapy to a patient against the current standard of care as well as in comparison to other treatments for other diseases.
Along with the QALY, NICE sometimes also includes other criteria such as the severity of the disease and end of life care. The QALY, however, lacks the level of sensitivity to consider the whole gamut of criteria which are involved in adopting a new medicine.
For example, there are benefits from addressing unmet need, the ability to work, reduced working-hours, disability pay, risk of rehospitalization, political policies and so on. How stakeholders prioritize that criteria are also not the same and this means that the value of 1 QALY is no longer the same as another.
Compared to the narrower application of the QALY, MCDA models are becoming an increasingly capable method for measuring the costs, benefits, and risks of a new intervention while also taking into consideration the different values of stakeholders. MCDA models are able to fine-tune results to give weighted consideration to higher-valued criteria in a transparent way and allow a kind of “what if…” analysis to test the impact of changes to key criteria.
This transparency and flexibility in MCDA modelling improves decision-making because it identifies the different values of stakeholders and structures all the criteria needed to make an informed decision.
MCDA is already extending its application to all areas of the life sciences. Numerous projects have been underway to understand and improve the use of MCDA models in benefit-risk assessments (BRA), health technology assessments (HTA), portfolio decision analysis (PDA), commissioning decisions/priority setting frameworks (PSFs), shared decision making (SDM), prioritizing patients’ access to health care and so forth.
Use Case
EMA launched a benefit-risk methodology project in 2009 to assess 1 qualitative generic method and 18 quantitative methods for measuring the benefits and risks of innovative medicines. The quantitative methods were organized into four groups, namely, simulation, models, statistics, and measurement methods. Only three from the models group were deemed adequate to quantitatively express the balance between benefit and risk – Bayesian statistics, decision trees and influence/relevance diagrams, and multi-criteria decision analysis (MCDA).
Simple MCDA models built in spreadsheets could be used effectively to support most decisions. More difficult decisions with numerous conflicting criteria would benefit from a more advanced software such as V·I·S·A, Logical Decisions, or Hiview3 which provide greater sensitivity and easy visualization for decision-makers.
For the EMA, but also other regulators, MCDA models will be one of the most useful tools for decision-making. Since, however, QALY models are a type of multi-criteria model, the EMA also saw a potential here for collaboration with HTA bodies. Regulators could include the QALY algorithm in their MCDA modelling and then pass on that model to HTAs ready to input local cost and budget criteria add-ons.
3. Managing Risk for Managed Entry Agreements (MEA)
As new drugs are reaching HTA and payers with insufficient evidence to reach the market, pharma companies are negotiating MEAs more and more to obtain market access and still achieve the best price possible.
MEAs grant access to a drug normally with conditions for the continued generation of more evidence and perhaps conditional lowering of the price. In this way, payers can more effectively manage the risk and uncertainty associated with a drug lacking proper evidence of its benefit.
Obviously, how a payer understands the value of an MEA scheme as well as risk and uncertainty will seriously affect the price of a medicine entering the market. How well does pharma look at the risk from the Payers side when developing a MEA?
Use Case
In 2015 the NICE Decision Support Unit (DSU) initiated a project to develop a three-point framework for analysing risk as applied to MEA schemes – payer uncertainty burden (PUB), payer strategy burden (PSB), and payer strategy and uncertainty burden (P-SUB).
P-SUB is the overall measurement of risk a payer incurs for a new therapy combined from the PUB and the PSB. PUB is a measure of the decision risk that this medicine is a less optimal decision based on the current evidence and price. PSB is a measure of the lower cost-effectiveness of a medicine in comparison to other current therapies.
The benefit of the P-SUB is to allow an easy, visual overview for decision makers to assess MEA schemes and risk in a consistent manner:
P-SUB Visualization. ISPOR. 27 Feb 2018, https://www.ispor.org/Event/GetReleasedPresentation/462
In the visualization, PSB is represented by red and PUB by blue. The more an intervention shows red, the more this suggests that a price-based MEA scheme could reduce the risk for a payer. On the other hand, the more of blue in an intervention, the more this points to the need for an evidence-based MEA to mitigate the uncertainty.
Any kind of MEA scheme is going to change both the PSB and PUB measurements to a certain extent. Furthermore, the specific kind of MEA design will also have a varied effect on the P-SUB which will allow the comparison of different kinds of MEA schemes on risk in a visual chart.
The P-SUB framework can improve the assessment of levels of risk for MEAs. It will allow pharma companies, HTA, and payers to work within a consistent framework for selecting specific MEA schemes and understanding their value and need.