The document discusses the complexities of forecasting in the oncology market, emphasizing the need for granular forecasting models to capture patient treatment processes and market dynamics. It outlines key challenges such as variability in input parameters and the importance of using patient-flow models to effectively predict treatment outcomes. It also highlights a case study of a global pharma client that implemented a macro-enabled interface for tracking and predicting revenue needs across multiple tumor types and countries.

1. May 2023
Navigating the complexity of
forecasting in the oncology market

2. © 2023 Indegene. All rights reserved. 2
Drug design and use in oncology is highly complex
One drug may be approved for multiple cancer
indications, perhaps carrying the same biomarker
Adenocarcinoma
of the lung
Glioblastoma
(brain and spinal cord)
Head and
neck cancer
Cancer drugs are designed to target
specific biomarkers or tumor types
The same drug may be approved for a different
line (or lines) of therapy in each cancer indication
Epidermal growth factor receptor (EGFR)
Platelet-derived growth factor receptor
(PDGF-R)
Mucin-16 (MUC-16)
Kirsten rat sarcoma virus (KRAS)
Cancers caused by EGFR mutations
Expanding product labels
line of
treatment
Adenocarcinom
a of the lung
Glioblastoma
(brain and spinal
cord)
Head and neck
cancer
2nd
line of
treatment
1st
line of
treatment
3rd
Challenge: The trial outcomes of new entrants can
dramatically change an indication’s standard of care
for each treatment line

3. © 2023 Indegene. All rights reserved. 3
Developing highly granular forecasts in this space is no easy feat
No. of patients (existing
+ potentially new)
Adenocarcinoma
of the lung
Glioblastoma
(brain and spinal cord)
Head and
neck cancer
line of
treatment
2nd
line of
treatment
1st
line of
treatment
3rd
% of patients likely to
discontinue or change
treatment
Estimated treatment
duration by drug, line of
therapy, etc.
No. of treatment naive
patients
% of patients likely to
discontinue or change
treatment
Estimated treatment
duration by drug, line of
therapy, etc.
No. of patients (existing
+ potentially new)
% of patients likely to move
between treatment lines
% of patients likely to
discontinue or change
treatment
Estimated treatment
duration by drug, line of
therapy, etc.
EGFR-focused
cancer drug
Remission and
relapse trends
Estimated
dosing
Estimated
dosing
Estimated
dosing
Changes
over time
Changes
over time
Changes
over time
Remission and
relapse trends
Remission and
relapse trends
and more
and more
and more
% of patients likely to move
between treatment lines
% of patients likely to move
between treatment lines

4. How can oncology forecasters best
tackle this level of granularity?
4
© 2023 Indegene. All rights reserved.

5. Getting oncology
forecasting right with
patient-flow models
5
• Captures a patient’s entire treatment
process
• Effectively models market complexities and
illuminates the mechanics behind them
• Includes all relevant drivers and decision
makers that determine which path of care
will be followed and when
• Provides a holistic view on the oncology
environment, and incorporates a wide
variety of input parameters that may change
dynamically over time
© 2023 Indegene. All rights reserved.
treated
untreated
relapse
remission
fail
relapse
death
maintain
relapse

6. Creating a patient-flow based model
Key elements
6
© 2023 Indegene. All rights reserved.

7. © 2023 Indegene. All rights reserved. 7
Split the patient population into smaller segments
Based on:
Cancer
incidence
Cancer
recurrence
Treatment
type
Biomarkers Line of
therapy
Cancer
severity

8. © 2023 Indegene. All rights reserved. 8
Understand dynamic patient flows
Stages of the disease Movement between lines of therapy Treatment and dosing regimen
Stage 1
Stage 2
Stage 3
Stage 4
Understand cross-line reductions; monitor rates of
progression, remission, and discontinuation
Which of these apply to my
product?
Monotherapy
Combination
therapy
Standard dosing
Dosing
adjustments
? ?
? ?
Diagnosis Starts
treatment A
Switches to
treatment B
Switches to
treatment C
Drops off
treatment

9. © 2023 Indegene. All rights reserved. 9
Understand dosing patterns
Induction dose
estimation
Each of these inputs require a different
kind of modeling
Dosing in oncology can be based on
Maintenance dose
estimation
Body surface
area
Weight-
based
Age-based Fixed dosing
Variable
dosing
Absolute dose-response relationship;
narrow therapeutic index
Even a slight variation in doses can be
life threatening or can compromise
response levels

10. © 2023 Indegene. All rights reserved. 10
Calculate the model monthly wherever possible
Understand clinical
trial endpoints
Determine clinical
treatment failure
Determine potential
subsequent lines of
therapy
Medication to
induce remission
Medication to
sustain remission
What is the course of
treatment?
Duration
(based on label,
adjusted for real-
world experience)
Duration
(based on label,
adjusted for real-
world experience)
Track monthly disease
progression
Differentiate between induction and
consolidation vs maintenance treatment
Capture frequency of drug
administration
Targeted cancer drugs approved in
the last 1-2 decades
Monitor the
frequency of
administration
Capture real-time impact
and estimate commercial
demand

11. © 2023 Indegene. All rights reserved. 11
Important factors and challenges to consider in
patient-flow modeling
• Quality outputs depend heavily on quality inputs
• It is important to examine how possible variability of
input parameters can affect forecast outputs
• Especially where inputs are dynamic and/or more
speculative
• Mitigating the risk of error in forecasts of this type can
take different forms, such as
• Inclusion of scenarios
• Simulation, such as Monte Carlo
• Creating a patient-based model whose structure is
heavily influenced by patient flow, rather than a pure
patient-flow model
A patient-flow model:
• Captures a patient’s entire treatment process
• Effectively models market complexities and illuminates
the mechanics behind them
• Includes all relevant drivers and decision makers that
determine which path of care will be followed and
when
• Provides a holistic view on the oncology environment,
and incorporates a wide variety of input parameters
that may change dynamically over time

12. © 2023 Indegene. All rights reserved. 12
Building the right infrastructure
Enable compilation of reporting outcomes
Leverage a cloud-based forecasting
platform (as appropriate) that is
equipped to handle the sheer volume of
granularity
Enable streamlined collaboration between
functions and geographies
Centralized operations
Provide a functionality to aggregate
portfolios worldwide
Focus on the right segmentation based on
assumptions, and create placeholders for
appropriate inputs
• Share uptakes
• Analogues
• Region mapping
• Product SKUs
• Survival rates
• Pricing
and more…
Move from offline, traditional
forecasting methods to the cloud
Gather detailed research outcomes from clinical
studies and market experience
Clinical attributes for share analysis
Clinical outcomes
Rates of disease progression, overall
survival, progression free survival
and more…

13. © 2023 Indegene. All rights reserved. 13
Maintain a consolidated repository of assumptions
Adenocarcinoma of
the lung
EGFR-focused
cancer drug
Early stage clinical
assets
Mid stage clinical
assets
Late stage clinical
assets Key inputs across
segments
• Epidemiology
• Market intelligence and
Insights
• R&D team
• Pricing and market access
• Marketing
• Finance
Single source of
truth
Glioblastoma
(brain and spinal cord)
Early stage clinical
assets
Mid stage clinical
assets
Late stage clinical
assets
Head and neck
cancer
Early stage clinical
assets
Mid stage clinical
assets
Late stage clinical
assets

14. © 2023 Indegene. All rights reserved. 14
How a global pharma client put this into action
Primary Objective: To understand, track, and predict revenue and supply needs of a specific molecule (a key target for multiple tumor
types) in over two dozen countries.
• Built a macro-enabled, fully-automated patient flow interface
engineered to run multiple scenarios based on evolving
assumptions
• Enabled a built-in template for standard assumptions to handle
tumor- and country-specific nuances with ease
• Built a global reporting dashboard with benchmarking features
and a consolidated, presentation-ready export functionality
Key Highlights Methodology
Analyzed key market parameters along with country- or
region-specific data inclusions and exclusions
Built a well-equipped infrastructure to accommodate multiple
tumor types, regions, and stakeholders in over two dozen
countries
Extracted and harmonized diverse data related to
epidemiology, overall and progression-free survival rates,
launch dates, peak shares, etc.
Designed a unique model for each tumor based on data
granularities, patient segmentations, and data frequencies
Activated robust analytics and reporting capabilities to
estimate revenues, and upside/downside scenarios
Outcomes
50% 5X 100%
Pre-validated
assumptions
Reduction in
decision-making time
Faster updates of
assumptions

15. Thank You!
To know more, write to us at connect@indegene.com