bio g strategic capacity assessment

© Bioproduction Group | www.bio-g.com 8/28/2013

Briefing:

Strategic Capacity Assessment (3-10 year time horizon)

Rick Johnston, Ph.D. Principal, Bioproduction Group Gary Wright Senior Account Director, Bioproduction Group [email protected]

mailto:[email protected]



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Bioproduction Group

Biomanufacturing

Operations Experts

Enterprise software and services

Consolidation and visualization of

production & capacity planning data

Real-Time Modeling System delivers

Debottlenecking

Process Optimization

Real-Time Scheduling

Quality by Design (QbD)

Capacity Planning

Improving Productivity, Flexibility, Quality

and Operations at the World’s Largest

Biomanufacturers

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Bio-G Real-Time Modeling System The next generation toolset for biomanufacturing analysis

Multiple

Real-Time

Data Sources

Consolidate,

View and

Analyze Data

Real-Time Model

of the

Complete System

Real-Time

Scheduling

Process

Optimization

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Building the right amount of capacity

Mantra in Biotech: “No patient shall go without”

• Drugs are typically life saving or preserving

• Products have a limited period of patent protection

• Manufacturing costs are typically 10%-20% of sale price

• Drug shortages (capacity < demand) have an outsize impact on share price

In short: high reliability / service levels are important

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However, the drug discovery and manufacturing process is fraught with uncertainty

• Uncertain clinical trial outcome (‘PTS’)

• Uncertain patient dosing

• Uncertain titer ranges

• Variability in supply capacity

• + many others

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Manufacturers must therefore manage significant variability, while also shielding the effects of that variability from their

customers.





Two tasks:

1. Accurately assess demand

2. Accurately assess capacity to meet demand for those products

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Build simple visualizations that allow us to

understand the impact of variability





Per Indication:

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One typical calculation path to determine demand

Patient Count Expected number of

patients per year

Dosage Assumption Baseline, as well as high / low

dosage assumptions

Titer Assumption Baseline, as well as high / low

titer assumptions

PTS Probability of technical success

KG demand / year The number of kg / year

needed to supply the patient population

Future Commercial Demand

Including Baseline / High / Low cases per indication

Safety Stock Assumption

Assume we build XXX days safety stock per product

Conversion Use characteristic curves:

batches to production days

Campaigning Can we make more than one

year of demand in one campaign?

Production Losses Harvest Yield, Purification Yield, Fill Yield, Pack Yield





Visualization 1: Histogram of Pipeline, with probabilities

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0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Like

liho

od

Number of Commercial Successes, 2010-2020

Histogram of Number of Successes by Indication





The business typically plans to a particular confidence level

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25% >8 indications

5% >=11 indications

50% >6 indications

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Like

liho

od

Number of Commercial Successes, 2010-2020

Histogram of Number of Successes by Indication




© Bioproduction Group | www.bio-g.com. Do not distribute without prior written permission. 0

5000

10000

15000

20000

25000

30000

35000

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026

KG D

eman

d (w

ith S

S)

Sensitivity Analysis: Future Commercial KG Demand (with SS) (Base Case Demand)

90%ile - Max

75%ile - 90%ile

50%ile - 75%ile

25%ile - 50%ile

10%ile - 25%ile

Min - 10%ile

Min

Expected

0

5000

10000

15000

20000

25000

30000

35000

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026

KG D

eman

d (w

ith S

S)

Sensitivity Analysis: Future Commercial KG Demand (with SS) (Base Case Demand)

90%ile - Max

75%ile - 90%ile

50%ile - 75%ile

25%ile - 50%ile

10%ile - 25%ile

Min - 10%ile

Min

Expected

There is also significant variability in future demand (driven by PTS)

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Plan for a high service level (90%ile of demand)

Demand by year (kg) at various confidence levels





Decision Tree Visualization: variability is resolved closer to launch

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June 2013 Jan 2014

Interim Analysis

Mar 2015

Competitor Launch

Competitor Launch

Dec 2016

Ph-III Clinical Trials





Visualization 2: Demand Pareto Chart

• We would try to include high, base and low cases for patient dosage and other variations in demand, conditioned on the product being approved.

• It is sorted by expected demand, so the largest ‘blockbusters’ are shown first

• This chart allows the impact of demand variability to be understood. Thicker bars mean there is more variability, and may indicate areas where more data or investigation is needed on a product (particularly if the expected demand is also high)

• We would probably expect to plan to a higher service level than the average, or mean, for most products in the pipeline.

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A Demand Pareto Chart shows the range of possible demand for a product over its lifetime (or standardized to a year).





Demand Pareto Chart

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0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

400.00

450.00

Tota

l Nu

mb

er

of

Ru

n S

tart

s 2

01

1-2

02

1

Indication

Top 30 Indications: Expected Total 12.5KL Run StartsIncluding Sensitivity in Dosage and Titer

84%ile-96%ile

72%ile-84%ile

68%ile-72%ile

32%ile-68%ile

28%ile-32%ile

16%ile-28%ile

4%ile-16%ile

Weighted Average

Large volume products (critical to plan accurately)

Shaded region indicates uncertainty in supply and demand for a particular indication

Dem

and

(ki

logr

ams

/ ye

ar)





*Reproduced from Tim Moore, Genentech, Presented at UC Berkeley Jan 2008

1. Titer Evolution:

Doubling every 18

months – 2 years

2. Significant

variability in titers

between products

Supply side: Titer variability

(bioreactor size) x (titer) x recovery rate = kilograms produced




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Titer vs. Kilograms D

em

and

in k

ilogr

ams

/ ye

ar (

kg)

Mega-blockbuster

Mid-range

Small Scale





De

man

d in

kilo

gram

s /

year

(kg

)

Projected Titer (grams / liter)

Mega-blockbuster

Mid-range

Small Scale

8+g/L 2-5g/L < 1g/L

Titer vs. Kilograms





De

man

d in

kilo

gram

s /

year

(kg

)

Projected Titer (grams / liter)

Mega-blockbuster

Mid-range

Small Scale

8+g/L 2-5g/L < 1g/L

“Black Swan”

Size of circle indicates comparative likelihood of launch

Titer vs. Kilograms





Supply Side: Capacity is not measured in lots per year.

That concept is now recognized to be fundamentally flawed:

• Making 10 batches of one product takes much less time than 1 batch of 10 products (due to changeover)

• High titer processes are more difficult to purify than low titer processes

• Variability in supply is significant, especially when considering the impact of contamination events.

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“Our facility can make 50 batches of product X in a year”





Supply Side: Use Characteristic Curves

• A simple visualization can be used called a characteristic curve

• This curve helps us understand the ability for the facility to supply different products and titers

• It incorporates the impact of variability and titer.

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Characteristic Curves allow us to understand the time in days, to make a certain amount of product.

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Nu

mb

er

of

com

ple

ted

bat

che

s

Day since 12KL Bioreactor Inoculation

Characteristic Curves 2 sample products

Product A, 2.5g/L

Product B, 5.0g/L

“It takes 91 days to produce 5 batches of product B. In the

same time we can produce 7 batches of product A.”





A Real Characteristic Curve

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Characteristic Curves also evaluate the impact of changeovers.

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“It will take us 201 days to make 7 batches of Product A and

5 batches of Product B.”

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Day since XX L Bioreactor Inoculation

Day 0:

Innoculation of 1st batch

in XX L Bioreactor

30 days to first

batch out

Day 88:

Innoculation of XX

L Bioreactor

Day 201: reach

production target of 5

batches

35 days to first

batch out

Day 58:

Innoculation of last batch

in XX L Bioreactor

Day 92:

last batch out

Product A Product B

Changeover

time

Product A

XX L Innoculation

Product B

XX L Innoculation

Product A

Batches Complete

Product B

Batches Complete





Occupancy Chart

• The chart is expressed in production days per year, and includes ramp-up, changeover and batch interarrival time metrics for each product.

• Days are used as the primary metric since they provide a unit of measure that is easy to understand. For example, if the occupancy time of the campaigns in a year is less than 365, the plan is feasible.

• If the occupancy time is more than 365 days, we can get an estimate of the number of ‘equivalent facilities’ we would need to accommodate demand. For example, if the occupancy time was 550 days, we would need another facility about half as large as the current one.

• This approach also allows the incorporation of facility utilization, for example a utilization of 90% would mean we plan to have no more than 328 days of occupancy.

• This diagram uses expected clinical demand and expected future commercial production, i.e. it does not explicitly consider risk.

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An Occupancy Chart shows the allocation of campaigns to the facility,

and plots how long each campaign will spend in the facility by year.





Sample Occupancy Charts

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“We have sufficient capacity until 2016. We may be able to

stretch out to 2017, with overtime and increased efficiencies.”

0 100 200 300 400 500 600

2013

2014

2015

2016

2017

2018

2019

2020

2021

Production Days

Facility Occupancy Chart

Product B

Product C

100% occupancy





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0 100 200 300 400 500 600

2013

2014

2015

2016

2017

2018

2019

2020

Production Days

Facility 1 Occupancy Chart

Product A

Product B

“Current Capacity is sufficient for our products.”

100% occupancy

Sample Occupancy Charts





0 100 200 300 400 500 600

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

Production Days

B633 Occupancy Chart

Synagis (RSV-633) Expected Future Commercial Production Expected Clinical Support

Overall Model produces an ‘occupancy chart’ of campaigns for expected future demand.

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Future Commercial

Demand

Expected Clinical

Demand

Current

Commercial

Demand

Overall occupancy chart, detailing the

number of production days in the

current or proposed facility





Summary of Visualizations

• Demand Planning

– Histogram of pipeline in aggregate

– Decision tree of pipeline by year

– Demand pareto chart (variability)

• Demand vs Supply

– Titer Variability

• Supply Planning

– Characteristic Curve

– Occupancy Charts

8/28/2013 26




bio g strategic capacity assessment

Documents

prior written permission

year of demand

supply capacity

production lossesharvest

purification yield

histogram of number

pack yield

impact of variability