1. Cancer Trials in India
Posited Challenges and Required Skills
Dr. Bhaswat S Chakraborty
Sr. VP, R&D, Cadila Pharmaceuticals
Ltd.

2. Contents
• Global Cancer Clinical Trials
• General challenges in design, conduct, analysis and interpretation of cancer
CTs
• Cancer CTs currently being conducted in India
• Challenges specific to India
– Funding of trials in India
– “Learn by hearing and doing” culture
• Lack of training in good documentation and reporting
– Perceptions about clinical trials in India
– Uneven expertise
– Following international templates
– Lack of databases that would facilitate
• Centre capacities and strength, recruitment rate and historical data
– Lack of recognition
– Evolving regulatory framework
• Some solutions

3. Global Cancer Trials

4. Cancer Trials (Phases I–IV)
• Highly complex trials involving cytotoxic drugs, moribund patients,
time dependent and censored variables
• Require prolonged observation of each patient
• Expensive, long term and resource intensive trials
• Heterogeneous patients at various stages of the disease
• Prognostic factors of non-metastasized and metastasized diseases are
different
• Adverse reactions are usually serious and frequently include death
• Ethical concerns are numerous and very serious
• Trial management is difficult and patient recruitment extremely
challenging
• Number of stopped trials (by DSMB or FDA) is very high
• Data analysis and interpretation are very difficult by any standard
• more………

5. Sample Size at Different Rates of Death
5000
4020
6 24
3040
8 48
2060
10 60 m1
1080 12 72
100 16
0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6
Relative risk
Alpha=0.5; Power =0.9; Accrual = 6 months; Follow up = 24 months
m1 is the median survival time on control arm

6. Accrual Time and Sample Size
3000
2440
6
1880
7
1320
8 A
760 10
200 12
0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20
Relative risk
Accrual Time has very Little Effect on Sample Size!!
Alpha=0.5; Power =0.9; Accrual = 6 months; Follow up = 24 months

7. Follow Up Time and Sample Size
3000
2440
6 24
1880 8 48
1320 10 72
760 12 120
16
200 F
0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20
Relative risk
Alpha=0.5; Power =0.9; Accrual = 6 months; median control survival
time=10 months

8. Ratio of Number of Patients in Control
vs Test and N
3000
2440
0.5 1.4
1880
0.6 1.8
1320
0.8 2 m
760 1 5
200 1.2
0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20
Relative risk
Alpha=0.5; Power =0.9; Accrual = 6 months; Follow up = 24 months
m is the ratio of number of patients in control arm to experimental arm

9. Loss of Power if N for Control arm
is Very Low
3000
0.5
2440 0.6
1880 0.7
1320 0.8
m
1
760
2
200
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
3
Power 0.2

10. Power vs Detectable Alternative
for Small Trials
1
0.8
10
0.6 12
0.4 16
m1
24
0.2
36
0
48
0 10 20 30 40 50 60
Detectable alternative
Alpha=0.5; Median Control Survival Time = 10 months; Accrual = 6
months; Follow up = 24 months; small sample size, e.g. 50-100

11. Sample Size vs Detectable Alternative
for Small Trials
200
170
10
140 12
110 16
m1
24
80
36
50
48
0 10 20 30 40 50 60
Detectable alternative
Alpha=0.5; Median Control Survival Time = 10 months; Accrual = 6
months; Follow up = 24 months; small sample size, e.g. 50-100

12. Power vs Detectable Alternative
for Large Trials
1
0.8
10
0.6 12
0.4 16
m1
24
0.2
36
0
48
0 10 20 30 40 50 60
Detectable alternative
Alpha=0.5; Median Control Survival Time = 10 months; Accrual = 6 months;
Follow up = 24 months; Large sample size, e.g. N≥1000

13. Bad or Wrong Methods of Analysis
• Comparison of life tables at one point in time ignoring their structure
elsewhere (except very rapid processes)
• If a few patients are at risk for more than a certain time but do not die, this
should not be taken as evidence of cure. Look at all the data of all the
patients
• Median survival times are not very reliable unless the death rate around
that median is very high
• A simple count of number of death in each group is inefficient as it ignores
the rate of death
• The best estimate of the probability of survival for a certain time (say 5
years), is given by the life table value at that time. Other simplistic
calculations may be misleading
• Randomized controls are always better than historical controls

14. Bad or Wrong Methods of Analysis contd.
• Estimation of survival is best done from randomization time. If it is done
from the time of 1st treatment it can be misleading (as initiating time for two
treatments can be different)
• Superficial comparison of the slopes of survival graphs as it biases the
proportion surviving at each given time
• Declaring ITT is better than per protocol analysis or the reverse
– Check all the data carefully especially the P values associated with
either type of analysis
• When you get an overall non-significant treatment effect, do not insist that
a sub-stratum can still benefit from the treatment even if that stratum
analysis is significant
• Realistically not checking the actual number of survivors on the last day of
the study (follow up)
• Be sure of your reason to use and report one-sided vs. two-sided t-tests

15. Total 256

18. Total 48 Trials

20. Total 155

21. Total 30

23. Funding of Trials in India - Projection
Source: IndiPharm projections based on information from The Boston Consulting Group and Business
Communications Co.

24. Funding of Indian Trials - Observations
• Majority of (part of) international trials in India are well funded
• Indian Govt. sponsored trials are also well funded
• This has contributed to
– Infrastucture of hospital and clinic-based research centres
– GCP training
– Institution of formal IRBs
– Better CRF filling and record keeping
• Some international and most national sponsors are still aggressive
“bargain-hunters”
• Perception of different qualities of trail design and data generation for
national and international Regulatory agencies
• Despite Sch. Y, non-harmonization in standards will be a huge burden for
future

25. Research Culture:
Traditional vs Participative
• Traditional
– Auditory instructions are followed better than written instructions
– Strong reflection abilities (than theorising and experimenting)
– Doing while learning, service oriented
– Record keeping and documentation are not always good
– Participation, discussion and questions are not encouraged highly
– Teachers and trainers are considered authorities
• Participative
– Open, friendly and novelty oriented
– Consensus oriented
– Theorising , hypothesising and experimentation encouraged
– Documentation is highly encouraged
– Trainers/teachers are friends and helpers

26. Perceptions about
Clinical Trials in India

27. Regulatory Framework
• Evolving, much better than what was 5 years ago
• The DCGI approval process
– Type A clinical trials (protocol approved by developed regulatory
authority e.g., U.S., Canada, U.K., Switzerland, Germany, Australia,
Japan, and South Africa); 4-6 weeks
– Type B trails are the rest; 8-12 weeks
• IRB approval and import licenses can be had in parallel with
DCGI review
• However, DCGI is still understaffed and domain specialists
are very few
• DCGI still has unique standards and precedence (e.g.,
acceptance of single arm CT as key evidence of S&E) which
need to be harmonized with international standards

28. Other Challenges Specific Indian
Oncology Researchers
• Training in design concepts and nuances
• International design templates but Local conditions
– Late reporting stage
– Unpredictable and uncharacterized social influences
– Informed consent may not be “free-willing”
– High levels of consent withdrawal
– Lack of Databases to estimate population baselines,
population effect size and variability for the standard
(control)

29. Challenges Specific Indian Oncology
Researchers
• Unpredictable recruitment rate
– Rural patients
– Poor and uninformed patients
– Inadequate counseling
• Varying expertise
– Competence
– Training
– Institutional support
– Lack of or improper understanding of responsibility and liability
– Inadequate DMCs
• Lack of recognition
– As leading trialists
– Low impact Indian journals in the pertinent areas
– Many inconveniences of regional centres and publicly funded institutions

30. Solutions
• Understanding and accepting the gaps
• Participation in international co-operative trials
• Extensive training
– Research methods
– Trial design
– Ethics
– Documentation skills
– Validation skills
– DMC
– Data analysis and interpretation
• Investigator and Site Development
– Not only sponsored by Pharma companies but also organised and
invested by Govt.
– Structured programs in universities and institutes

31. Solutions…
• Curriculum development
• People and domain networking
• Publications
– In high impact journals
– Strategizing improvement of Indian journals
• Conferences and other common forum
– Like this one
• Govt.-academia-industry ventures and (noble) incentivization
• Encouraging creativity and higher education
• ……………

32. Thank You Very Much