This document discusses challenges and opportunities in the pharmaceutical industry. It notes that R&D costs are high due to extensive outsourcing, and benchmarking outsourced R&D units could lower costs by 40%. It also describes a software tool called PAT that would help predict side effects and check patent uniqueness of new drug formulas, saving 40% of time wasted on failed experiments. Overall the document analyzes ways to improve innovation and lower costs in pharmaceutical R&D through analytical solutions.

1. PHARMACEUTICALS
PADMAKAR GUNTUR
VAMSIDHAR M
KRISHNA MUTTOJU
PANKURI JAIN
SWATI BAJAJ
HIMA BINDHU

2. Insights
Within R&D there is need of superior innovation and
lower costs which is achieved through:
 A comprehensive understanding of how the human
body works at the molecular level.
 A much better grasp of the killer-effects from
consumptions of drugs including side effects and
intricacies caused
 Greater collaboration between the industry,
academia, the regulators, governments and
healthcare providers.

3. Insights….

4. Business Problem-1
• It is acknowledged that the pharmaceuticals industry has enormous
expenditure in building a successful molecular composition in a time
frame. R&D activity which is extensively outsourced predominantly
occupies a great share of the incurred cost.

5. Analytical consultation
• Bench marking of the outsourced R&D
– The R&D divisions per phase , per drug, per disease are benchmarked
to indentify the best and the worst performing units resulting in the
significant reduction of the outsourcing costs.
– The analytical solution provides atleast a 40 %
saving
100
80
(in millions)
60
40
20
0
average R&D cost per unit Average cost after bench
marking
Cost saved is 40%

6. Cost model
average Estimated cost for a molecule
composition 1 Billion $
average Number of phases 10
average number of R&D units per
phase 10
average cost /R&D unit 10 M$
expected savings if the 4 least
performing units are stopped funding 40 M$
Estimated cost for benchmarking 0.3 M$
Projected price for providing this
analysis 4 M$

7. Technical analysis For Benchmark
• DATA TYPE: Disease(cat), Drug(cat), Costs Incurred(neu),Cost, Time
Period(neu), Number of Failures before successful experiment(neu), Team
Size(neu), Successful/Failure Experiment (cat), R&D Firm(cat), R&D Firm
Demographics(cat), R&D Firm Size(neu), etc
• DATA VOLUME: 20 to 40 input and output parameters
• IMPLEMENTATION: Quantify the level/positioning of each R&D Firm and
identify top most firms for outsourcing.
• PROBLEM CLASS: Linear Optimization
• TECHNIQUE: DEA

8. Business Problem-2
The formula of the created molecular composition many a time goes to
trash as the usage results many a side effect apart the suffering of patent
uniqueness.

9. Pharma Killer effects and patent tool
(PAT -- Pharma analytic tool)
• The software tool that we would be providing would explain the pharma
scientist the possible side effects and its intensity for a molecular
composition.
• It also validates the formula for its uniqueness by checking with the patent
database
• Maintenance and service is entertained for this tool.
• Based on the data generation, the tool would upgrade itself and provide
the updated results.

10. Cost model for PAT
Domain understanding 1 month
Collecting and processing of the data 3 months
Analysis and training 3 months
Testing 2 months
Integration 2 months
Design 1 months
Cost per hour 80 $
Cost of the tool PAT 0.6 M $
Support per hour 60 $

11. Technical analysis for PAT
• DATA TYPE: Disease(cat), Drug(cat), Molecule Composition(cat),Costs
Incurred(neu), Time Period(neu), Number of Failures before successful
experiment(neu), Side effects and/or Intricacies if any (cat)
• DATA VOLUME: One Record for each type of molecule composition. Patent
database.
• IMPLEMENTATION: Given the molecule composition of a drug, to classify whether
Side effects and/or Intricacies is caused or not. And if caused then which out of
given Side effects and/or Intricacies categories. Patent search,
• ERROR MEASURE: RECALL
• PROBLEM CLASS: CLASSIFICATION
• TECHNIQUE: DECISION TREE,NAÏVE BAYES, RANDOM FOREST, Page ranking, K-NN
using HADOOP

12. PAT Framework

13. No of Rejected experiments at approval board

14. The tool saves the 40 % time wastage in killer experiments and
patent findings.
40%
Time consumed
Killer experiments

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