Project Management National Conference 2011                                  PMI India  Risk Analysis and Mitigation Model...
Project Management National Conference 2011                                                                        PMI Ind...
Project Management National Conference 2011                                          PMI India                 Abstract: R...
Project Management National Conference 2011                                           PMI India                 product li...
Project Management National Conference 2011                                           PMI India                 This Pharm...
Project Management National Conference 2011                                         PMI India                 calculating ...
Project Management National Conference 2011                                           PMI India                 Risk disco...
Project Management National Conference 2011                                               PMI India                 the ri...
Project Management National Conference 2011                                           PMI India                 Regulation...
Project Management National Conference 2011                                                                 PMI India     ...
Project Management National Conference 2011                                          PMI India                 In this pha...
Project Management National Conference 2011                                         PMI India                 analyzing th...
Project Management National Conference 2011                                           PMI India                 Whereas, A...
Project Management National Conference 2011                                            PMI India                 TF (R) fo...
Project Management National Conference 2011                                               PMI India                 The si...
Project Management National Conference 2011                                                           PMI India           ...
Project Management National Conference 2011                                  PMI India                 Srikanth.Pasham@asi...
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  1. 1. Project Management National Conference 2011 PMI India Risk Analysis and Mitigation Model in PLM Projects (Life Sciences and Pharma) Pasham, Srikanth Program Manager – (EMEA) Xchanging2|P a g e Application of Select Tools of Psychology for Effective Project Management
  2. 2. Project Management National Conference 2011 PMI India 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:................................................................................................................173|P a g e Application of Select Tools of Psychology for Effective Project Management
  3. 3. Project Management National Conference 2011 PMI India 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 the4|P a g e Application of Select Tools of Psychology for Effective Project Management
  4. 4. Project Management National Conference 2011 PMI India 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.5|P a g e Application of Select Tools of Psychology for Effective Project Management
  5. 5. Project Management National Conference 2011 PMI India 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 for6|P a g e Application of Select Tools of Psychology for Effective Project Management
  6. 6. Project Management National Conference 2011 PMI India 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.7|P a g e Application of Select Tools of Psychology for Effective Project Management
  7. 7. Project Management National Conference 2011 PMI India 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 result8|P a g e Application of Select Tools of Psychology for Effective Project Management
  8. 8. Project Management National Conference 2011 PMI India 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:9|P a g e Application of Select Tools of Psychology for Effective Project Management
  9. 9. Project Management National Conference 2011 PMI India 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.10|P a g e Application of Select Tools of Psychology for Effective Project Management
  10. 10. Project Management National Conference 2011 PMI India 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.11|P a g e Application of Select Tools of Psychology for Effective Project Management
  11. 11. Project Management National Conference 2011 PMI India 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 while12|P a g e Application of Select Tools of Psychology for Effective Project Management
  12. 12. Project Management National Conference 2011 PMI India 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))*1013|P a g e Application of Select Tools of Psychology for Effective Project Management
  13. 13. Project Management National Conference 2011 PMI India 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:14|P a g e Application of Select Tools of Psychology for Effective Project Management
  14. 14. Project Management National Conference 2011 PMI India 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 Trails15|P a g e Application of Select Tools of Psychology for Effective Project Management
  15. 15. Project Management National Conference 2011 PMI India 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.16|P a g e Application of Select Tools of Psychology for Effective Project Management
  16. 16. Project Management National Conference 2011 PMI India [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.17|P a g e Application of Select Tools of Psychology for Effective Project Management
  17. 17. Project Management National Conference 2011 PMI India Srikanth.Pasham@asia.xchanging.com18|P a g e Application of Select Tools of Psychology for Effective Project Management

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