Line of Therapy Analytics: A key to commercial drug success

Lines of Therapy

Patient-care related overtones and the interplay between payers, providers, patients, governments and producers have made Life Sciences and Healthcare a complex system. But what simplifies it is the Real World Data as this can help pharma and biotech companies mine trends in real world product usage and align their marketing, market access, medical affairs and commercial operations to maximize patient benefit and revenues. In this blog, Saama’s Rajeev Gangal explains how Line of Therapy analytics can help the Life Sciences industry position the drugs better in the market and thus reach larger patient pool and maximize RoI.

With changing healthcare paradigms and stringent regulations, what remains constant, is the way we perceive diseases, and that is, as an enemy waging short-intense (Infections) or perennial (chronic) battles.  It’s perhaps thus natural to think of treatments in terms of Lines of Attack or Defense and to contain or manage the enemy or defeat it as in “the war on cancer”.

Hitherto, the physicians tried older therapies first and then moved on to the newer drugs, mostly to prevent emergence of resistance, and also prescribe drugs that were economically viable.  It’s obviously desirable that a drug, highly efficacious with the least side-effects, useful for most patients and cost effective, be tried first. This is where Line of Therapy comes to picture.

In general, Lines of Therapy (LOT) are based on what would be the most effective treatment modality for a patient, for a particular diagnosis, given severity or stage of the disease and probable response to the therapy. The LOT for a given disease, represents a consensus in the medical community, about the sequence of therapies or treatment given to patients based on their disease state.

Hence, it is important to note that there is no fixed time for which each line of treatment is given. When patients progress from one line to the next, it may be because their condition improves, or in case of cancer, the opposite.

Here’s a recent example of approval for immuno-therapeutics, thought to be the next blockbuster area in Oncology:

Lines of Therapy

As seen above, in Oncology, drugs are typically approved by FDA for a specific line of therapy, based on evidence from clinical trials. The label may also be restricted to patient segments that have a particular biomarker (PD-L1). This makes treatable patient populations increasingly narrow and reduces the market potential of the drugs.

How does Line of Therapy Analytics help?

For commercial success and recouping R&D expenditure, it becomes imperative that drugs are approved for the first LOT rather than latter ones. The prognosis and the drug utilization may decrease with higher lines of therapy. First line drugs also stand a better chance of support from payers for a higher price point.

While patient outcome metrics are extremely important, the primary objective is to optimize drug share in a given LOT. Overtime the market share in an approved LOT may be influenced not only by patient outcomes, but also by payer acceptance (cost), competition, effects of combination with upstream, downstream and concurrent regimens and other factors. It’s thus important to track KPI’s like new patient share, physician counts, geographical and payer trends among others.

Marketing teams need to use “Line of Therapy” analytics to help optimize drug sales in a given LOT by providing intelligence to Sales and Medical Affairs. This can also be used to provide better outcomes that can be utilized as springboards for Health Economics and Outcomes Research (HEOR).

Marketing science and other teams that analyze claims and EHR data have their own business rules to define Lines of Therapy, based on the market basket of drug for a particular indication, for eg. Non-Small Cell Lung Cancer. The market basket of drugs contains all commercially relevant drugs used for a given diagnosis.

Another important aspect of this analytics is the ability to select patient population based on inclusion and exclusion criteria. Selection of eligible patients and ability to define custom LOT’s can help generate Patient-LOT histories as also calculation of various KPI’s mentioned earlier.

Why LOT analytics? Because it answers the ‘What Ifs’, that can be:

  • What’s the effect on commercial KPI’s if we change the age eligibility criteria?
  • What’s the effect on patient outcomes if drug ‘X’ is used concomitantly with drug ‘Y’?
  • What is our new patient share and how is it influenced by drugs in the market basket and our eligibility criteria?

Claims and EHR data is voluminous and every incremental update adds to the complexity of data processing and analytics. Specialized analysts in centralized CoE’s are generally tasked with querying this data, processing ETL pipelines as per “Lines of Therapy”, defined by business users. There is a natural inertia in the analytics services pipeline given this service model.

Given its modality, LOT analytics can help business users define their own market baskets, eligibility rules and LOT’s in a flexible manner using a web based user interface. It gives an easy access to powerful analytics techniques and answers ‘What-If’ questions.  LOT analytics also automates repeat analytics on Terabytes of data irrespective of the underlying big-data technologies.

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About Rajeev Gangal

mmMr. Rajeev Gangal - Program Director, Life Sciences

Rajeev brings with him 20+ years of impeccable blend of technical and functional expertise. He joined Saama as the Head of the Life Sciences and Healthcare practice, which is now the fastest growing business unit. Currently he heads the Center of Excellence (CoE) division to bring forth cutting-edge technologies and solutions and partnering with top MNCs to drive significant business value.


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