Planning for demand failure: A dynamic lot size model for clinical trial supply chains

Abstract

This paper examines the optimal production lot size decisions for clinical trial supply chains. One unique aspect of clinical trial supply chains is the risk of failure, meaning that the investigational drug is proven unsafe or ineffective during human testing and the trial is halted. Upon failure, any unused inventory is essentially wasted and needs to be destroyed. To avoid waste, manufacturers could produce small lot sizes. However, high production setup costs lead manufacturers to opt for large lot sizes and few setups. To optimally balance this tradeoff of waste and destruction versus production inefficiency, this paper generalizes the Wagner–Whitin model (W–W model) to incorporate the risk of failure. We show that this stochastic model, referred to as the failure-risk model, is equivalent to the deterministic W–W model if one adjusts the cost parameters properly to reflect failure and destruction costs. We find that increasing failure rates lead to reduced lot sizes and that properly incorporating the risk of failure into clinical trial drug production can lead to substantial cost savings as compared to the W–W model without the properly adjusted parameters.

Introduction

For every new drug that reaches a pharmacy’s shelf, roughly 5000–10,000 other potential medicines have failed to achieve commercialization (Pharmaceutical Research and Manufacturers of America, 2009). For a pharmaceutical or bio-tech company attempting to create a new medicine or treatment, failure is not a surprise, but rather an event to be planned for. In this paper, we analyze the impact of failure during clinical trials on the production-inventory decisions for investigational drugs and discover that an extension of the Wagner–Whitin model (Wagner and Whitin, 1958) can greatly improve efficiency in the clinical trial supply chain.

One of the most important hurdles prior to the US Food and Drug Administration’s (FDA) approval of a new drug is the testing of a drug candidate in clinical trials. Three phases of clinical trials are usually required to test both safety and efficacy of a potential treatment in human subjects. Typically, Phase I involves 50–100 healthy individuals, Phase II recruits a few hundred potential patients, and Phase III seeks to test the drug candidate in a few thousand patients. While we may know how many patients are needed in each phase of the clinical trial, there is an inherent uncertainty associated with each trial: the risk of failure. Indeed, only 21.5% of drug candidates entering clinical trials actually achieve FDA approval (DiMasi et al., 2003). Many of these drug candidates that fail to pass through the clinical trial hurdle are well documented in the financial press. Below is just one example from the New York Times (Berenson, 2006):

The news came to Pfizer’s chief scientist, Dr. John L. LaMattina, as he was showering at 7 a.m. Saturday: the company’s most promising experimental drug, intended to treat heart disease, actually caused an increase in deaths and heart problems. Eighty-two people had died so far in a clinical trial, versus 51 people in the same trial who had not taken it.

Within hours, Pfizer, the world’s largest drug maker, told more than 100 trial investigators to stop giving patients the drug, called torcetrapib. Shortly after 9 p.m. Saturday, Pfizer announced that it had pulled the plug on the medicine entirely, turning the company’s nearly $1 billion investment in it into a total loss.

The small success rate of clinical trials is painful to a pharmaceutical company’s balance sheet because of the enormous amounts of time, labor, and materials required to perform a clinical trial. On average, 37% of the $100 billion R&D spending by pharmaceutical companies is spent on the clinical trial process (Cutting Edge Information, 2004, Thomson CenterWatch, 2007). Annual supply chain spending for drugs under clinical trials can be substantial, e.g., accounting for 20% or more of a company’s research and development spending.¹ For just one drug candidate, a company can spend millions of dollars every quarter to produce supplies for just one clinical trial. When failure in a clinical trial occurs, every dollar spent on manufacturing, packaging, and distribution of unused clinical trial supplies is wasted and in most cases, unused material must be returned to a proper disposal facility for destruction (English and Ma, 2007). For example, Cotherix Inc., estimated $126,000 in destruction costs for an obsolete drug that was valued at $1.5 million (Cotherix Inc., 2006).

It would be unfair of us to label all post-failure drug supply as waste. Inventory is needed to ensure that as patients are recruited to participate in the study, drug supply is available. Any delays in this phase of testing become one less day of patent protection available to the drug. According to Clemento (1999), every extra day of patent availability is worth $1 million for a typical drug. Since patient recruitment is the typical bottleneck in conducting clinical trials, a shortage of clinical drug is considered an unacceptable delay and our model assumes no backlogging of demand. That being said, one would usually be economically foolish to produce enough supply to support all three phases of a clinical trial at once.

Production of investigational drugs is typically characterized by high costs (both fixed and variable) due to the low demand volume, low yield and the premature manufacturing process. In addition, at each step in the synthesis of the chemical compounds, rigorous quality control procedures are required to ensure that investigational drugs “are consistently produced and controlled to the quality standards appropriate to their intended use” (George, 2005). Often, active ingredient production for a drug candidate is a costly process and may require unique manufacturing equipment and processes. Thus, both the fixed and variable production costs tend to be much higher for investigational drugs than approved drugs which have been scaled up for mass production.

In this paper, we present a mathematical model for production planning to balance the two opposing forces of (1) high fixed production costs pushing for large lot sizes and (2) high failure costs which favor smaller lot sizes. High fixed costs for production, in the form of both time and money, lend support to producing large lot sizes. Alternatively, the high risk of failure, the high production variable cost and inventory carrying cost argue for smaller lot sizes. Smaller lot sizes would avoid wasting unused clinical drug supplies as well as the significant cost of destroying the unused material, but can result in high costs due to multiple production setups and more numerous quality control activities. We accommodate this environment by generalizing the Wagner–Whitin (W–W) model (Wagner and Whitin, 1958) to incorporate a stochastic component, namely, the risk of failure. We will refer to this model as the failure-risk model. By investigating the failure-risk model, we are able to make the following contributions:

• We demonstrate how every failure-risk model is equivalent to a corresponding deterministic W–W model if one adjusts the cost parameters properly to reflect failure risk and destruction costs, so many classic results of the W–W model still apply. Most interestingly, the planning horizon theorem indicates that in the failure-risk model, updating failure probabilities as the clinical trial proceeds does not affect the optimal supply decisions under certain conditions.

• We conduct a comprehensive numerical study using various environments that clinical trial manufacturers may face. We show that the failure-risk model can lead to substantial costs savings as compared to using the W–W model which ignores the risk of failure.

The remainder of this paper is organized as follows. We review the related literature in Section 2. The model and analysis are presented in Section 3, and their extensions are discussed in Section 4. The potential benefits of properly accounting for failure are shown in an illustrative example in Section 5. A more thorough numerical study to test the effectiveness of the model under real-world scenarios is performed in Section 6 with implications within industry discussed in Section 7. Finally, we summarize the paper and discuss future research directions in Section 8.