ETPM1

Project Management National Conference 2011 PMI India

Risk Analysis and Mitigation Model
in PLM Projects (Life Sciences and
Pharma)

Pasham, Srikanth
Program Manager – (EMEA)
Xchanging

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Application of Select Tools of Psychology for Effective Project Management


Contents

1 INTRODUCTION..............................................................................................................4
2 RISK INFLUENCE FACTOR ANALYSIS & RISK MANAGEMENT BY PLM
Pharma Tools:........................................................................................................................6
3 RISK NETWORK GENERATION IN CLINICAL TRAIL PROCESS by PLM Tools:..7
4 RISK PRIORITIZATION IN CLINICAL TRAIL PROCESS by PLM Tools:...............11
5 Conclusions:.....................................................................................................................16
6 References........................................................................................................................16
7 Authors Profile:................................................................................................................17

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Abstract: Risk Analysis is crucial process the PLM projects in the Life
Science and Pharma industry. Present risk analysis models for life
sciences projects analyze the risks independently and are static in nature,
since they do not take into consideration of one risk on the another and as
a result it becomes very challenging when we compute the likelihood and
consequences models to forecast the risks in the future. This Risk Models
are Core PLM objectives in the Life Science and Pharma Industry: Develop
economical formulations and drug manufacturing processes.
Collaboration between the clinical and operational Organizations to drug
deliverables with acceptable timelines. Deliver First Time Right controls
and processes as per FDA guidelines. To determine the product success
earlier in the life cycle with better product intelligence. Decision Making
Models to track Clinical and Non Clinical Performance in real time across
the geographies. Hence this model in the areas of life sciences and
Pharma introduces the concepts of risk dependencies with a mathematical
model which makes risk analysis algorithms to predictable in the future.

Index Terms: PLM, Simulation Models, Clinical Trails, Clinical Risk
Dependency Factor & Mitigation Factor and Drug Discovery

1 INTRODUCTION

The term risk has its own meaning and perspective in different Pharma and Life
Sciences business processes. Risk can also be viewed as an opportunistic to
gain competitive advantage in areas of Pharma drug discovery, clinical trails, and
regulatory compliance. Based on this insight we are defining risk as a probable
event which has negative or positive impact on the Pharma & Life Sciences
processes or Pharma Project objective in the drug discovery.

Risk due to increasing internal and external complexity in managing the entire
product lifecycle from product inception to phase out due to nature of Pharma
companies working in silos of information across the functional areas. In case
some of the Pharma R &D organizations cross functional information flow is
either lacking or non-existent.

Risk is due to no single data source for products and related information due to
variety of different data sources and lack of collaboration across the organization.

Risk management continues to be a challenge creating significant business
impact when deficiencies are indentified during regulatory audits. Early
awareness of risk events and immediately assessing the impact across the

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product life cycle will provide the foundation to leverage the risk management
resulting in improved best business performance possible.

Taking a proactive approach to risk management. Some of the best Pharma
companies hold suppliers to a level of risk management equal to their internal
production facilities. Rather than taking a reactive approach to supplier risk
management, relying on reviews of batch records and infrequent formal audits,
these companies adopt a proactive, on-site risk assessment and problem solving
approach across the globe.

Some of the Pharma companies use this model of risk “heat maps” for
company’s own knowledge of process risks predicting the parts of a supplier’s
operation that have the largest potential to create problems. These heat maps
can be used to identify critical criteria during supplier selection, and companies
can engage directly with their existing suppliers to agree on appropriate risk
management and mitigation techniques to ensure regulatory compliance.

For critical suppliers, top Pharma companies map the full operational taxonomy
of past, current, and future risk in detail and carefully manage to those risks. One
pharmaceutical manufacturer, for Example, developed a detailed risk
management heat map for its own plants, allowing it to focus quality
improvement efforts precisely where the biggest risks arose. Having proved the
technique in-house, the company is now rolling out the same management and
mitigation approach to its most important suppliers. The ability to manage risk
and compliance throughout the supply chain will be more crucial than ever
before. While globalization is increasing the risks, greater public awareness and
more diligent enforcement are raising the bar. The business case for
virtualization is clear. It enables a company to shift to a flexible cost base, reduce
the risks associated with investing in new assets and access new technologies
and skills. It also helps it align its supply chain network with its demand forecasts,
transfer the risk of primary and backup supply to a third party and drive costs
down by switching products and processes between competing suppliers in its
network. In order to manage the risks associated with collaboration, virtual
manufacturers will need to ensure they have access to real-time data from every
stakeholder in their supply chains. Robust risk assessment and risk-management
capabilities across the extended supply chain which can be managed by PLM
Pharma Tools.

This Paper proposes an Integrated Approach to Risk Analysis of Pharma
Process which is more planned, opportunistic and robustic. The proposed risk
analysis Pharma model concentrates on the analysis part of the risk
management Pharma process and offers a logically structured way to quantify
and mitigate risk to bring the drug to market with compliance in a record time.

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This Pharma and Life Sciences article is organized as follows:

a. Risk Influence Factor, which deals with the vulnerability of the system with
respect to the external factors like regulatory compliance in various countries
for Pharma process in drug discovery, clinical trails and drug manufacturing /
packaging.

b. Risk prioritization in each phase of the clinical trails in each of the phases is
analyzed based on the cost benefit and time to bring it to the market in
various geographies.

c. Risk dependency analysis in each of the phase during the drug development
process based on the risk mitigation models and algorithms which have been
developed specific Pharma process as per the regulatory compliance.

d. Risk Network Generation Pharma Models are defined as: What -if and Why
Analysis which is a risk in the drug formulations and discovery process.

e. Risk Mitigation Analysis and Models signifies mitigation variation effort in
different Pharma process with respect to concept to commercialization of the
drug as per regulatory compliance.

2 RISK INFLUENCE FACTOR ANALYSIS &
RISK MANAGEMENT BY PLM Pharma Tools:

Risk Influence Factor (RIF) in clinical trails signifies all the factors that are
influencing the risk category as per the present conditions under which the
project is taken and decided to launch in that geography. This plays a significant
role in estimating the basic dimensions of a risk in terms of probability of risk
occurrences in the specific process of clinical trails phase in terms of probability
of risk occurrences. RIF’s analysis for clinical trails process can be best
represented by Bayesian Networks in which each of the RIF represents a parent
node to a risk node which is identified during the clinical trails phase. All RIFs
should be mapped with respect to the time of occurrence of upcoming risk in
different phases of clinical trails. Clinical Phases Trails 1, Clinical Phase Trails 2
and Clinical Phase Trails 3 can be the some of the RIFs in the Clinical Trail
Phase of Drug Discovery. Hence we can conclude that the more influential are
RIFs for a risk category, the more chances of risk to happen in that category and
impact depends on the current phase of the clinical trails. Thus RIFs portray the
basic picture of risks which is helpful in estimating the parameters like probability
of risk occurrence during the clinical trails phase of the project. The formula for
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calculating the probability of occurrence of a risk individually is computed as
follows:

Probability occurrence of risk during the clinical trial Phase: P(R) = P (R| PA
(RRIF).

Where PA (RRIF) represents all the RIF acting on the risk of particular category of
drug discovery.

To ensure that a risk management approach is applied to allocating FDA
inspectional resources, some of the agencies are developing a quantitative risk-
based site-selection model for use in choosing sites for inspection. This model
will help to predict where its inspections are most likely to achieve the greatest
public health impact using the PLM tools. Also developed action plans for the
review and revision of field compliance programs to incorporate risk-based
approaches to improve transparency and guide FDA investigators in conducting
inspections including the preapproval inspection program and active
pharmaceutical ingredients (API) program.

3 RISK NETWORK GENERATION IN
CLINICAL TRAIL PROCESS by PLM Tools:

All Risk Models in the Drug Development Process is based on the subjective
notion of probability and assumptions. Hence this model is based on the
following assumption in clinical trails phase:

The exposure of risk E(R) is a function of time, regulation and mitigation effort
(M), mathematically it can expressed as: E(R) = f (t, r, M).

During the initial phase 1 subjects are healthy and not potentially complicated
patients, hence the exposure of risk is less and mitigation is almost negligible
(M~0).

During the phase 2, proof of concept falls short of expectation due to the
increase of exposure of Risk and needs a mitigation algorithm.

During the phase 3, the risks increases exponentially since this phase are more
complex and presents with additional set of new risks and needs a complex
mitigation algorithm. Here the drug is administered in more realistic clinical
settings and includes the regulations changes under ICH, GCP and EU
legislation.

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Risk discovery is an important activity in risk analysis process which applies to all
phases during the drug discovery phases of the project. The process of risk
discovery has to be done in frequent iterations due to the complex nature of the
clinical trails phase of the project. This includes the two variants: What happens if
and Why Analysis, Risk Dependency Analysis during drug discovery.

This approach takes the input of “what happens if “scenario for each of the risk
category in the drug discovery project. This scenario defines the potential fault
points during the clinical trails phase of the project and “Why” provides proper
validation and authentication of for the risk discovered during different clinical
trials phases. In the drug discovery project, we can clearly define this. “What
happens if” Regulations Change (Risk Category: FDA Compliance), “Why” Drug
Specifications is not approved. What happens if: Less No of Scientists, “Why”:
Due to Collaboration using the PLM tool during the drug discovery process.
Hence in both the scenarios, it is clear that it helps us to discover and identify
new risks like Regulation Changes and Too Less Scientists. These scenarios are
conducted recursively till the “Why” does not generate any more risks that are
non compliance with the regulations for the drug discovery process.

Risk Dependency Analysis during the Clinical Trail Process. This is the
fundamental activity in the proposed risk mitigation model during the clinical trail
process for drug discovery. The risk should not be treated as an individual entity
as it is perceived in most of the current risk models rather than it must be
perceived as related entities which overall influences the decision making
process in the mitigation algorithms for the different phases of the drug discovery
from concept to commercialization. This model identifies the dependencies
between the risks and analysis risks based on the identified relationships during
the clinical trail phased of the project. We develop algorithm for generating risk
network for different scenarios discussed above.

The Risk set identified by “what happens if” and “Why” process is defined as
follows:

R = {R1, R2, R3, R4 …Rn} : To Generate Simulation Model by PLM

This algorithm discovers new risks which are validated by “what happens if” and
“Why” process and builds the network of all the risks which are critical during the
different clinical trails phases. To elaborate the risk dependency analysis
comprehensively, we consider a generic example in which R1, R2, R3, R4 and
R5 are identified in the risk discovery phase of clinical trails. For each risk
discovered R1 is taken first and it is observed that R2 and R5 (belong to the
same category of R1) and R3 belong to the different category but has influence
on R1. Risk R2 is affected by R4 and R5 and R6 is found as new risk in the
process of analyzing. Risk R6 affects R3 and R5 in the process and as a result

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the risk network tree can be generated in the clinical trail process. This can
mathematically defined as

D=(X, E), : To Generate Simulation Model by PLM

Where X= {R1, N and R is all the risk discovered in the clinical trail phase}

D: is the risk node which is defined by the attributes like risk category, risk
exposure, time for mitigation and total mitigation factor

E = {Ri < --- Rj: Rj is affecting Ri with the clinical dependency env factor Cji}

This clinical risk network which is obtained is a directed acylic graph (DAG)
which inherits the Bayesian Characteristics.

The risk dependency analysis also helps in identifying the new risks. In a critical
drug discovery process it was identified that patient participation is minimal then
it is identified as a potential risk. If it is identified as a risk then as per discussion
below the risk dependency process helps us to identify the potential new risks.

Parent Risk: Patient Involvement

Risk Category: Patient Category.

The process of risk dependency for discovery of new risks as follows in the
clinical trails phase:

Step 1: Identify child risk that can add strength to parent risk within the parent
category.

After analyzing the various factor in the patient domain. It can be inferred that
patient acceptance could be potential risk as they might not acceptance the
clinical design, because of the reason that their participation was minimal (i.e. not
visiting the clinical facility as required every week).

Step 2: Identify the risks of other categories that may after the parent risk. It
could be:

Project specific factor:

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Regulations / Compliance changes during the drug discovery
process

There is maximum chance that the regulations of a country may change which is
due to the patient risk in a particular geography.

Quality category:

Quality Compliance during the drug discovery process

If the patient involvement is minimal then there are chances that it might affect
the overall quality of the product as expected by patient.

These new identified clinical risks should be validated through WHAT HAPPENS
IF AND WHY analysis before considering them as a potential risk. Thus risk
dependency identification might help us in identifying new potential clinical risk.
Again the above example is just a demonstration; conditions may differ from drug
discovery project in 3rd world countries to developed world projects. For example
in some clinical projects there might not be any lethal problem if the patient
involvement is low. Therefore in those clinical projects there is no question of
analyzing this further. Here it can be inferred that risk is very subjective concept
which assumes different meaning in different context. It should be viewed
relatively than absolutely as it is done is current models operating inside a clinical
lab.

Clinical Risk Dependency Factor can be computed between the two risks (Rm
and Rn) by a dependency factor Cmn. Hence Cmn can be computed as follows:

Cmn = (P(Rn| Rm) ) * (I(Rm) / ( I(Rn) + I(Rm) ) ) : To Generate Simulation
Model by PLM

Where, P (Rn| Rm) represents clinical conditional probability of Rn risk when Rm
has already occurred and (I (Rm) / (I (Rn) + I (Rm)) represents the contribution of
regulation risk impact over compliance risk. It can be figured out that for strong
dependencies, the probabilities of occurrence of compliance risk shows high
degree of correlation with regulation risk. That is, if there is high probability of
occurrence regulation risk then there will be high chances of compliance risk to
happen when connected via strong dependency relationship. Thus co-relation
helps us to estimate and validate the probability of occurrence during clinical
trails.

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Clinical Risk Dependency Simulation Model

1
0.9
0.8
Risk Dependency Factor

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0 0.5 1 1.5 2 2.5 3 3.5
Clinica l Tria l Pha se s

Fig 2.0: Clinical Risk Dependency Simulation Model using PLM Pharma
Customized Tool.

The above risk generation mathematical models can be simulated by developing
specific algorithms during different clinical trails phases by customizing the PLM
tools.

4 RISK PRIORITIZATION IN CLINICAL TRAIL
PROCESS by PLM Tools:

After the risk is discovered and its attributes are defined, the next step is to
prioritize the risk during the different clinical trail phases. This model makes use
of the current clinical assumption and clinical regulations described in paper.
Hence, this model takes impact as prime factor to prioritize the risk and it takes
other clinical parameters which mitigates this risk which may be due patient non
cooperation, regulatory changes or compliance changes across the geographies.
This model makes use of the risk exposure and mitigation exposure during
clinical trails.

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In this phase of risk analysis, clinical risk exposure is calculated which signifies
how much a process is exposed to a given risk. This is calculated by simple
formula:

RE (Rc) =Impact * probability of occurrence during clinical trails

Here, Impact of a risk on clinical trail phase must be analyzed based on the
effect of a risk on regulatory and compliance goals. Therefore, for each goal
impact should be graded on scale of 1-5. And the final impact of risk will be:

Impact (Ri) =∑Impact (Regulatory and Compliance Goals)/∑j + dEnvij:

Note: We shall get a smoother curve if we take integrated values and more
authentic results.

Risk Prioritization Models by Simulation Models using PLM Pharma
(Customized) Tools.

Here, ∑ Impact (Regulatory and Compliance Goals) is summation of impact on
all goals for a risk Ri, ∑ j is total number of goals identified and (dEnv) the
derivative environment changes in a drug discovery project. Thus Impact of a risk
is average of its impact on the goals with the environmental changes where the
clinical trails are conducted. Probability of occurrence signifies the chance of
occurrence of risk during clinical trails phase. It should be noted that while

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analyzing the clinical risk dependency the Impact and probability of occurrence
should satisfy the inequalities mentioned in the above equation.

Risk Mitigation Exposure during the clinical trail phases of drug discovery
by PLM Tools:

We need to shift the corporate compliance strategy from reactive processes to
more proactive approaches, seeking to meet all published guidelines (like FDA)
for current markets where drug is going to be commercially made available.
Monitor and assess product compliance more frequently, and monitor
compliance during the product design process with the collaboration Tools of
PLM.

We need to begin gathering data on product composition levels for currently
restricted substances and developing product-level compliance data for those
substances using PLM tools.

We need to perform mock clinical audits to determine ability to meet clinical
documentation and verification needs. Also need to address potential errors in
compliance analysis and variability in supply chain content reporting accuracy by
periodically auditing content using PLM Tools.

We need to develop common best practices that span departmental boundaries
(at a minimum) across the global R&D Organizations using PLM Tools.

We need to ensure that compliance documentation from suppliers is captured
and managed in association with products and the product structures using PLM
tools.

Finally acquire appropriate PLM and specialty tools to develop a common source
of product data and to help enable engineers to design for compliance across the
different geographies.

Mitigation Simulation Algorithms (using PLM Pharma Tools) can be
developed for computing Cost Benefit Factors (CBF), Time to Market Factors,
Patent Factors and Compliance Factors using PLM Tools.

We can develop mitigation simulation algorithm (using PLM Pharma tools) for
this cost benefit factor which describes the relative cost benefit associated with
the risk and can be defined as follows:

CBF (R) for Clinical Trails = (1- (Cost of Mitigation/Amount on stake))*10

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Whereas, Amount on stake, can be calculated through the standard econometric
clinical methods. Apart from this, the amount of stake can be looked as the risk
category fraction in the clinical project cost that it carries away. It can be useful
when the nature of risk is subjective, that is, when there is no way to calculate
the amount on stake objectively through any standard econometric clinical
methods. It can be calculated by another simulation algorithm (using PLM
Pharma Tools)

Amount on Stake (during Clinical Trails) = Cost Impact Factor (during Clinical
Trails) * Risk category share in project cost (during Clinical Trails)

Whereas the Cost impact factor to be graded on scale of 0.1 – 1.0, this
represents the impact of the risk on the risk category cost in project: Low (0.1 to
0.3), Medium (0.4 to 0.7) & High (>0.8)

Fig 3.1 Cost Benefit / Investments for doing a typical Drug Development
Project.

Cost of Mitigation can be computed using different tools to mitigate the clinical
risks & other costs.

The time factor can be simulated using mitigation algorithms which signifies the
relative time factor associated with the clinical risk. It is based on the assumption
that various clinical risks which have higher time of mitigation should come low in
mitigation priority during the clinical trail phases. The mitigation time factor for
clinical trails can be computed using mathematical models as follows:
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TF (R) for Clinical Trails = (ETM/TTM) + Cr : To Generate Simulation
Model by PLM

Where,

a. Total time for mitigation (TTM) is the time period in working hrs from when
the clinical risk is identified till the time the given clinical risk is expected to
trigger.

b. Expected time for Mitigation (ETM) is the time required to mitigate the clinical
risk to a large extent.

c. Cr is computed based depends on the Clinical Env under which this clinical
risks are evaluated.

Note: For constant risk which does not varies with the time should be given
constant value for the above ratio analyzing the above part.

Fig 3.1: Risk Mitigation Simulation Model using Customized Pharma PLM Tool

Mitigation Exposure Clinical Risk can be computed using the mathematical or
simulation algorithm which is the summation of CBF (R) for Clinical Trails and TF
(R) for Clinical Trails

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The simulation model will include risk factors relating to the facility (such as the
compliance history) and to the type of drugs manufactured at the facility. The
simulation model will also include risk factors relating to the manufacturing
processes and the level of process understanding by the global organizations.

PLM Pharma tools help regulators to take risks in clinical trails during the drug
discovery development. The leading national and multinational agencies have
become much more cautious about approving truly innovative medicines, in the
wake of the problems with Vioxx since there are gaps in the business processes
mapped to the current software being used by these organizations.

The regulator may decide whether or not to license a medicine using specific
risk/benefit analyses using simulations models. It will ask sponsoring companies
to disclose the gaps in their knowledge about the risks associated with any
medicines they submit for approval, and it will make reimbursement of new
therapies contingent on performance using collaborative tools like PLM Pharma.

Despite significant effort by many companies, the risk level for compliance is still
widely varied by company since appropriate mitigation models using specific
PLM tools for Pharma are not in place.

5 Conclusions:

The Simulated Risk Analysis Algorithms enlists all the risk parameters that
is used by existing risk analysis models in software project management
and introduces concept of clinical risk dependency during different clinical
phases of the drug discovery process, which came out to be beneficial and
crucial factor in simulation of clinical risk analysis during the drug
development. It is shown that how to identify the various dependencies
between clinical risks and how it can be used in simulation risk models
through a series of mathematical formulations which can be simulated and
the simulations of mitigation algorithms at any point of time can be
predicted using the PLM Pharma (customized)Tools. Therefore it is
compliant, robust and futuristic than the current static models of clinical
risk analysis in the Pharma & Life Sciences world.

6 References

[1] XUE-HUI REN, YUAN-HUA LI,HONG-XIA TIAN, Study on Environmental Risk Influence Factor
of Tongliao, Applied Artificial (pp 678-685).World Scientific.

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[2] J. PEARL, "Aspects of Graphical Models Connected With Causality," UCLA Cognitive Systems
Laboratory, Technical Report (R-195-LL). In Proceedings of the 49th Session of the
International Statistical Institute, Italy, Tome LV, Book 1, Florence, 399-401, August 1993.

[3] ONY KENNEDY, Pharmaceutical Project Management Second Edition, Informa Healthcare.

[4] K. NUMMALLY and JOHN S. McCONNELL, Six Sigma in the Pharmaceutical Industry.

[5] RATNESHWAR JHA, Risk Analysis and Mitigation Precedence Model, Infosys Technologies,
Mysore.

[6] ANTHONY SCOTT BROWN, Clinical Trials Risk: a new assessment tool, Royal Cornwall
Hospitals NHS Trust, Truro, UK

[7]. RIC PHILIPSAND KEVIN SACHS Pharmaceutical Manufacturing, Supply Management: New
Game, New Rules, MCKINSEY & CO

[8]. The Product Compliance Benchmark September 2006 Report by Aberdeen Group

7 Authors Profile:

Srikanth Pasham has about 18 years of experience in handling large
projects of which 10 years in providing Project Management skills to
Global Application Delivery groups. He is dynamic, result oriented and
performance driven, with good communication skills, interpersonal skills,
technical skills, project / program management skills, delivery
management skills to manage multiple client accounts and a commitment
to meet client project goals under tough deadlines and high-pressure
environment. He is PMP Certified Manager with Post Graduate Diploma
in Software Engineering.

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Srikanth.Pasham@asia.xchanging.com

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