Lean Six Sigma Principles in drug discovery research

Dr Suguna Nandgaonkar and Dr Ankasha TejamThursday, November 22, 2012, 08:00 Hrs  [IST]

Sustaining and succeeding in an environment which is driven by high cost pressures, thrust for continued innovation and high unpredictability in terms of success and ROI is the real -time challenge which pharma industry is currently facing. Gone are those days where they have been rewarded for incremental innovations. Pharma companies not only have to bring high quality candidate drugs to market but also need to prove constantly that their brand adds value to patients & healthcare. There is tremendous competition in terms of the quality of their product portfolio and health services they offer in their sales bucket rather than just mere pills. The most contributing phase for the success is the discovery phase of lead optimization which ensures the entry of promising candidates for further development. However with the increase rate of late stage failures there has been significant concern around overall productivity of this phase of the research. This article focuses on the new facade of conventional drug discovery aligned to Lean Six Sigma methodology. The application of Lean Principles and Six Sigma methodology can facilitate streamlining process metrics issues related to various phases of discovery experiments, reducing errors and the number of repetitive experiments thereby providing high quality data in short turnaround time and investment costs.

As many as four out of 10 drugs identified as potential lead candidates during lead optimization stage fail in late stage clinical trials. Clinical forecasting has become extremely unpredictable to ensure late-phase safety and efficacy based on earlier-phase research data with sufficient accuracy to facilitate early termination of eventual failures and late stage attrition. In many cases the cause of failure for candidate drugs at late stage clinical development phase is still unknown. In 1991 the major causes of the clinical failures of drugs were evaluated, it was understood that 40per cent  of them were due to unacceptable pharmacokinetic characteristics unidentified in the early stage research owing to process errors and repetitive analytical errors. This has eventually lead to shrinking pipeline and declining trend in patent filing due to lack of novel candidate drugs.

Space for chart
It is crucial for pharma companies to re-examine their approach and methodology during early stage research to sustain and maintain the flow of compounds through development pipeline. The dire need for innovation and standardization of discovery research will be key ingredients for long- term success for pharma ensuring only promising and quality candidate drugs proceed further for clinical development, thus avoiding waste of significant investment in terms of cost, time and resources.

Productivity of the drug discovery has been disappointing in recent years despite the significant increase in the R&D spending through introduction of novel technologies. Rising cost and increased rate of failures being the major drivers for this declining trend in productivity and becoming a major threat to the overall industry business model.

Drug discovery today
Drug discovery research involves significant resources and investment from multiple scientific research units and stake -holders at diverse phases of early drug research. Each research unit involved produces large amounts of data to be captured and analyzed from multiple experiments. The effective integration of data and scientific knowledge from many disparate sources is very crucial to make valid scientific conclusions through multiple iterations in order to make it a success. Repetitive research processes by multiple stake holders carried out in conventional methods at various phases will inevitably lead to errors and thus poor data quality, increased cycle time and investment cost. Poor quality data also leads to incorrect or invalid decisions regarding further investments on potential candidates.

The conventional methods lack predefined metrics to measure
n    process performance to take appropriate initiatives for improvement.
n    productivity of assets, people & inventory
n    quality in terms of the research output and data.
n    cycle time to assess the time spent on non value added activities.
In recent times the discovery phase has become a prime focus of the improvement initiatives of the pharma industry. Industry has begun to realize the principle of designing quality at an early stage rather than relying upon results of clinical studies and believing in the end product testing.

Lean Six Sigma and its approach to drug discovery
Lean Six Sigma methodologies can certainly change the facade of conventional drug discovery through process optimization, improved productivity, enhancing operational performance and reduced cycle time of drug discovery research organizations. The benefits of Lean Six Sigma are historically known and proven across other industries and even pharma industry has began to realize the same.

Lean refers to investigating the potential for removal of non-value adding activities from the processes while Six Sigma attempts to improve the activities that must be done. In recent times it has been realized that the combination of both these approaches would maximize benefits for the organization.

Lean focuses on process speed and efficiency by identifying and eliminating process problems. It's like an improvement engine for any organization where there is continuous flow of activities in a organized fashion. Lean aims to drive down cycle times and retaining processes and sub-processes that add value while trimming or eliminating those activities that do not.

Six Sigma was originally developed by Motorola to identify and remove the causes of defects and errors in manufacturing and business processes. It uses a set of quality management methods, including statistical methods, and creates a special infrastructure of people within the organization who are experts in these methods. At its core Six Sigma uses statistical metrics to reduce defects in a known process. It describes five steps to process improvement Define, Measure, Analyze, Improve and Control; abbreviated to DMAIC

Space for chart
Lean and Six Sigma are both process improvement methodologies that have been used throughout industries as varied as the healthcare industry to car manufacturing. Lean Six Sigma combines two most significant trends of current industry needs a. making work better (using 6sigma) and b. work faster (using lean principles)

The long discovery life cycle can be subdivided into sub processes and metrics that can be analyzed. By applying Six Sigma principle, the process performance can be measured by defining metrics and by applying Lean principles non- value added activities, under a given process can be eliminated. In drug discovery, metrics may be defined in two ways, one is process throughput to measure the ultimate outcome of the process in terms of productivity and second is cycle time measurements to measure the total cycle time required for a particular process.

Process throughout can be anything such as the number of molecules advanced per thousand screened compounds or the percentage of compounds from medicinal chemistry or combinatorial libraries meeting specific pharmacokinetic or toxicology criteria. Cycle time measurements will help to enhance the process efficiency by reducing or eliminating defects errors and also redundant steps in a laboratory process. It may evaluate the opportunities for automation. A given laboratory process may be holding back the enterprise effort if it is prone to errors and rework is often necessary. Laboratory automation can be a tool for minimizing human errors in some cases.

Lean Six Sigma application in discovery research
In the process map of the Toxicity experiment given below, we have identified different levels of activities aligned as per Lean Six Sigma principles. The identification of potential results from toxicity experiments in a planned manner has both scientific and business implications. Scientific implications in terms of how this data proves as quality input to the next stage of experiments and business implications in terms the cost, time and resources.

One common experiment has been planned by four different research units while the next stage research plans are varied for each team. In a conventional set up each team would have planned to identify the experimentation method, detail protocol for experimentation, order and indent for chemicals or reagents, availability of equipment and skilled resources to conduct the experimentation.

Expected outcome for each team in this experiment and the quality of results will vary subject to the availability of skilled resources, quality of raw materials, use of validates test methods, qualification & calibration of equipments. Thus leading to high cycle time, high costs of investment and repetitive poor quality data. Lack of business skills for team co-ordination, information sharing and decisions making also will impact the final experimental outcome.

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Lean Six Sigma methodology will facilitate all the four teams to define, measure, analyze, control and improve the complete experimentation process within stipulated timelines, where the entire activity will be executed with structured planning, excellent team work, effective communications and knowledge exchange among team members to execute the experiment successfully.

Early tools and techniques utilized for Lean Six Sigma were limited to manufacturing and supply chain, which gradually emerged as universal methods of streamlining the activities within any organization, thereby improving the productivity and efficiency of the process system in a very sequential manner proving huge revenue benefits. This methodology also helped in preventing several pain points and developing more robust methods of processing data and analysis statistically. Several large pharma and health care organizations have benefited in terms of speed and accuracy of the data generated and standardization of processes used. The application of lean Six Sigma formula in drug discovery research in organizations likes Pfizer, GE, Novartis etc has fetched in huge profits. The methodology is so robust that it provides due advantage to innovation and standardization which are key points for success in drug discovery.

Applying Lean Six Sigma tools and techniques to drug discovery R&D enables pharma and biotech industries to derive the following potential benefits.
n    Eliminate waste and redundancy
n    Curtail variation in process streams
n    Improving the quality, reliability and reproducibility of research outcomes
n    Reduced cycle time (product development)
n    Strategic design plans for experimentation
n    Lower overall development cost and resource utilization
n    Better results that work faster and provide more efficient results that are robust to  variations.

These improvements will be propagated and perhaps even augmented upstream and/or downstream of the drug discovery and development pipeline, generating significant gains in terms of data quality, process alignments, operational efficiency, resource conservation and overall productivity.

Authors are working with Infosys Technologies Limited.