Decision-Support Tool for Risk Analysis in Biopharmaceutical Manufacture

Published by Elsevier Science on behalf of IFAC

DECISION-SUPPORT TOOL FOR RISK ANALYSIS IN BIOPHARMACEUTICAL MANUFACTURE Suzanne S. Farida, John Washbrookb , Nigel Titchener-Hooker3

"The Advanced Centre for Biochemical Engineering, Department of Biochemical Engineering, University College London, Torrington Place, London WCl E 7lE. UK. sfarid@uc!.ac.uk bDepartment of Computer Science, University College London, Cower Street, London WClE 6BT, UK.

A decision-support tool is presented for modelling both the technical and business aspects of biopharmaceutical manufacture. The use of the tool for risk analysis is demonstrated through a case study that uses Monte Carlo simulation to imitate the randomness inherent in manufacturing subject to technical and market uncertainties. The analysis demonstrated the range in possible outcomes for the project throughput and cost of goods and the likelihood that these metrics exceed a critical threshold. The example illustrates the benefits to companies of using such a tool to improve management of their R&D portfolio to control the cost of goods. Computer-aided simulation; Decision support systems; Manufacturing processes; Biotechnology; Risk; Uncertainty; Economic design

1.

INTRODUCTION

improvement by process development and effective R&D portfolio management.

Accelerating the drug development process while controlling costs is of critical importance in the biopharmaceutical industry, now faced with shortened product life cycles due to increased competition from generic drugs. The time that companies have to recoup their investment is shrinking while the costs of developing drugs is rising (Gerson, et ai., 1998). Hence to achieve an acceptable return on this investment, biopharmaceutical companies need to focus on cutting down the cost of drug development and improving the overall time-to-market.

Managing an R&D portfolio to control the cost of goods is complicated by the fact that each project is subject to technical and market uncertainties. Incorporating the effects of risk analysis helps enhance the quality of decision-making within a company. The need for computer-aided simulation tools, capable of capturing both the technical and business aspects of manufacturing processes, is critical for such decision-making. In this paper a prototype tool, developed at The Advanced Centre for Biochemical Engineering at University College London (Farid. et al., 2(00), is used to model biopharmaceutical manufacture for clinical trial material preparation under uncertainty.

Biopharmaceutical companies typically have a portfolio of drug candidates to manufacture for clinical trials, but with finite resources, budget and capacity. The cost of manufactured goods is one of the factors affecting the probability of overall corporate economic success that is capable of

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Sample outcomes are randomly generated by using the probability distribution for each uncertain quantity and then utilised to determine a trial outcome for the model. Repeating this sampling process a large number of times leads to a frequency distribution of trial outcomes for a desired measure of merit. The resulting frequency distribution can then be used to make probabilistic statements about the original problem.

The remainder of the paper is structured as follows. In Section 2 a brief overview of decision-making under risk is given. The prototype tool is described in Section 3. In Section 4, a case study is presented to illustrate how the tool can be used to help biopharmaceutical companies manage the preparation of clinical trial material with fluctuating demands, titres and market successes.

2.

DEALING WITH UNCERTAINTY

The case study presented in Section 4 illustrates the use of the Monte Carlo simulation procedure to imitate the randomness inherent in biopharmaceutical manufacture subject to fluctuating product demands , titres and market successes.

Production of material for clinical trials is a manufacturing area in which design strategies must be evolved that pay particular attention to risk factors that may impact cost and delivery time. The key sources of uncertainties affecting the manufacture of biopharmaceutical candidates are technical and market related uncertainties. Examples of such technical uncertainties include the product titre during fermentation, the purification yield and the duration of the manufacturing tasks. Market uncertainties can be characterised by fluctuating clinical trial demands for material. Risk assessment provides a methodology to estimate the reliability of outputs and to quantify the likelihood of exceeding a specified threshold.

3.

The prototype tool introduces a hierarchical approach to represent the key tasks in a manufacturing process . It was created in ReThink Version 3.1 (Gensym Corporation, Cambridge, MA), that runs as a layered application on top of G2 , a graphical object-oriented programming environment. Since the necessary building blocks specific to bi opharmaceutical manufacture are not parI of Ihe basic 1001 set provided by ReThink, il was necessary 10 customi se ReThink to model the processes of biopharmaceutical manufacture.

There are numerous methods for taking uncertainty into account. Sensitivity analysis is often employed to determine the behaviour of performance measures to ± x% changes in each uncertain factor and hence determine the stability of the base case. For more complicated problems where it is possible to estimate probability functions for uncertain factors, Monte Carlo simulation is a practical way of determining the impact of the project uncertainties. Monte Carlo simulation generates random outcomes for probabilistic factors so as to imitate the randomness inherent in the original problem. In this manner a solution to a rather complex problem can be inferred. Product & ancillary task sequences

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The object-oriented representation makes it easier to transform rapidly knowledge about a process into a graphical model that is easy to understand and use. The user generates models of a manufacturing process by simply clonin g the customised objects (eg. fermenter resources, fermentation tasks) from palettes and dropping them onto workspaces. The objects' attributes are then configured for the specific case and the task blocks connected together to create a running model.

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inputs were determined from literature or vendor sources.

The key inputs and outputs to the modelling tool are discussed below and summarised in Figure 1. Setting up a specific manufacturing case starts with the specification of the resources within the plant and their capacities and costs. The resources include staff, equipment, materials and utilities. This specification provides the initial constraints on resource availability for use in the manufacturing process. The task sequences are then defined for each of the recipes to make the product, prepare the intermediates and prepare the equipment. The resource requirements to execute the recipes are configured by allocating the resources to the tasks and specifying their utilization. The model compares these resource demands against the resource availability and schedules when resources can be used . It is necessary to input the parameters for the mass balance and cost calculations. Finally the product demand is specified to determine the number of batches required per campaign. After a particular case is set up the impact of different production strategies on the process performance, resource utilization and bottlenecks, and the resultant cost of goods can be evaluated . When performing Monte Carlo simulations additional inputs include the probability distributions of the uncertain factors, the length of each simulation and the number of simulations required .

4.

Table 1 specifies the key uncertamtles considered, together with estimates of their discrete probability distributions. Table 1 Key risk factorsand their probability distributions Risk factor Product titre

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The titre defines the grams of product expressed per litre of fermentation broth and significantly influences the manufacturing costs as it determines the number of batches required to satisfy the product demand. Typical antibody titres in batch cultures are 0.1-0.5g/L (Walsh, 1998). However, higher antibody titres in the order of 1-3 grams per litre have been reported using fed-batch cultures of mammalian cells (Birch, et aI., 1995; Bibila and Robinson, 1995 ; Xie and Wang, 1996). Six representative titres were therefore selected to represent typical titres at Phase 1 clinical trials, considering that titres in the order of grams per litre were unlikely at this stage since they require optimization of feeding strategies which may not be a priority at such an early stage. The titres were assumed to follow a discrete normal distribution as indicated in Table 1.

CASE STUDY

4.1 Set-up A hypothetical case study that examines the impact of fluctuations in key technical and market factors on the cost of goods and throughput will now be presented. The example is based on a biopharmaceutical company with a portfolio of antibody candidates to be manufactured for Phase I clinical trials. They have designed a pilot plant based on mammalian cell culture processes for clinical trial material production. The company wishes to estimate their annual cost of goods and throughput, in terms of the number of campaigns, and determine the impact of key risk factors .

Demands for Phase 1 clinical trials vary from milligram quantities to tens of grams (Walsh, 1998) depending on the cumulative dose of the drug and the number of patients in the trial. The amount of product that needs to be manufactured must take account of both uses of the drug candidate. Nonclinical usage addresses quality control and validation issues, such as the need for in-process samples, release and stability samples, and retain samples. Projections of investigational product requirements can be made assuming a 25-300% excess over the actual subject usage is necessary for non-clinical uses (Bernstein and Hamrell, 2000). The values selected for the manufacturing product demands were assumed to follow a discrete normal distribution.

The key assumptions are given below. The pilot plant has a single production train to handle the portfolio of projects for clinical trial material preparation. It has been designed assuming a typical product titre of 0.4 g/L, which yields 45g of product per batch after purification. It is initially assumed that a typical product demand for Phase 1 clinical trials is 45g and hence single batch campaigns produce enough material to satisfy the demand . The plant operates seven days a week and 48 weeks a year with a turnaround time of four days between campaigns. The purification process is principally based on chromatographic and filtration techniques. The cost

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An additional uncertainty is whether a drug candidate in development will reach the market. The 1996 figures from the Tuft Center for the Study of Drug Development revealed that only 23% of drugs entering clinical trials became marketed drugs (Breggar, 1996). Other authors provide slightly more optimistic success rates from Phase I to launch of 34% (Mackler and Gamerman, 1996) and 67% (Struck, 1994). For this analysis the probability of market success was assumed to be 25%.

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The prototype tool was used to model the manufacturing activities within the pilot plant capturing both the technical and business aspects of the process. Each simulation lasted 48 weeks and the key performance measures were the cost of goods per gram, the number of campaigns squeezed out of the plant and the number of drug candidates that reach the market. Having validated the results of a single simulation, several simulations were performed to characterize the variability in the key performance measures due to uncertainties in the product demand, titre and market success. Frequency distributions of the performance measures were generated .

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The direct cost of goods per gram for each of the manufacturing tasks during a single batch campaign is illustrated in Figure 2. This indicates that in the base case (titre = OAg/L, demand = 45g), the cost of material, utilities and staff resources are concentrated in the chromatography steps. The figure also deqIonstrates that the ancillary tasks, such as c1eaning-in-place (CIP) procedures, consume a significant amount of resources. Such details may not have been transparent to the company before such an analysis; presenting costs on a task basis helps to focus cost reduction efforts on specific tasks.

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4.2 Simulation results and discussion

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Examination of the frequency distributions for the products that reach the market, shown in Figure 4, can help to determine whether attempts to improve the throughput are necessary. The data in figure 4 indicates that the probability of getting greater than a single product to market is only 20% and that the maximum number of product successes is only three. To assure the probability that each year's development efforts yield at least two product successes, the company's minimum throughput will need to be five campaigns. Since the risk of failing to

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Fig. 2. The direct cost of goods per gram on a task basis for a single batch campaign

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developments of the tool include exte.nding its capabilities to assess the impact of uncertainties on profitability indicators such as the net present value (NPV). Such simulation-based risk analysis becomes even more useful when comparing alternative manufacturing strategies. Future work will address examples of these, such as comparing the benefits of investing in a pilot plant utilizing disposable equipment as opposed to stainless steel equipment.

manage at least five campaigns is 55%, the company may consider investigating the trade-off between increased process development efforts to increase the titres and the associated cost and time penalties. Other options include determining the cost benefit of increasing the capacity of the plant or deciding criteria when project rejection or termination is desired .

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ACKNOWLEDGEMENT

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Financial support from the Biotechnology and Biological Sciences Research Council (BBSRC) and Lonza Biologies is gratefully acknowledged.

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REFERENCES annual cost of goods per gram (arbitrary units)

Bernstein, D. F. and M . R. HamreIl (2000). Integrating drug supply issues with strategic preclinical and clinical development. Drug In! 1.,34,909-917. Bibila, T. A. and D. R. Robinson (1995). In pursuit of the optimal fed-batch process for monoclonal antibody production. Biotechnol. Prog., 11, 113. Birch, J.R., J. Bonnerjea, S. Flatman, S. Vranch, (1995). Production of monoclonal antibodies. In . MonocLonal antibodies: Principles and Applications. (J. R. Birch, E. S. Lennox" Ed.), pp. 231-265, Wiley-Liss, Inc., New York. Breggar, M. M.(1996). Navigating regulatory maze requires a proactive strategy. 1996 GEN Guides Editorial,277-8. Farid, S., J. L. Novais, S. Karri, J. Washbrook, N. J. Titchener-Hooker (2000). A tool for modelling strategic decisions in cell culture manufacturing. Biotechnol. Prog., 16, 829-836. Gerson, D. F. G., V. Himes, R. Hopper, L. Khandke, F. Kohn, A. Komotar, P. Krumm, J. Machulski, A. Weisser and S. Sciotto-Brown (1998). Transfer of processes from development to manufacturing. Drug Inf, l ., 32, 19-26. Mackler, B.F.; G. E. Gamerman (1996). A perspective on global approval strategies in the 1990s. 1996 GEN Guides Editorial, 264-6. Struck, M. M. (1994). Biopharmaceutical R&D success rates and development times. BiofFechnology 12,675-677. Walsh, G. (1998). Biopharmaceuticals: Biochemistry and biotechnology; Chap. 2, Chap. 10, John Wiley: Chichester. Xie, L. and D. I. C. Wang (1996). High cell density and high monoclonal antibody production through medium design and rational control in a bioreactor. Biotechnol. Bioeng., 51, 725-729.

Fig. 5. The frequency distribution and cumulative frequency curve for the annual cost of goods per gram. The cost outputs generated by the Monte Carlo simulations are depicted in Figure 5. This highlights that although the frequency distribution is positively skewed, the dispersion of the cost of goods per gram outcomes is considerable. These results can be used to determine the likelihood of exceeding a particular cost of goods. The threshold value may be dictated by the present R&D budget in the company or by considering the future worth or profitability of the company. These outputs draw attention to the need to reduce the variance in the cost of goods per gram. The above analysis of the outcomes of the Monte Carlo simulation highlights the benefits of incorporating uncertamtles when evaluating manufacturing and portfolio management strategies. The information generated by the simulation studies can provide key support to decision-makers.

5.

CONCLUSIONS

The application of a prototype decision-support tool for modelling the operation of a biopharmaceutical manufacturing plant under uncertainties has been presented. The effect of fluctuating product demands and titres on the performance of a biopharmaceutical company manufacturing clinical trial material were analysed using the Monte Carlo simulation technique. The impact of these uncertainties on the cost of goods and annual project throughput was determined. Effective use of the simulation outcomes can lead to concentrated R&D efforts, more effective use of resources, faster time-to-market and improved overall corporate economic performance. Further

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