Genisphere and MultiCell Partner to Deliver miRNA Therapeutic using Dendrimers

Genisphere and MultiCell Technologies established a collaboration through which they will  investigate the use of Genisphere’s patented 3DNA® Dendrimer nanoparticle drug delivery technology to enable the targeted delivery of MultiCell’s MCT-485 candidate to liver tumours. MCT-485 is a noncoding double stranded micro RNA (miRNA) that has demonstrated oncolytic and immune stimulating activity in in vitro models of hepatocellular carcinoma.

Genisphere’s 3DNA® Dendrimers are made from systematically assembled DNA strands. The firm is exploiting the technology to improve sensitivity in immunoassay and nucleic acid detection platforms, as well as to deliver therapeutics in a highly specific manner.

Clinical-stage biopharma Multicell’s lead drug candidate, MCT-125, is an oral small molecule drug combination which targets the noradrenaline-adrenaline neurotransmitter pathway for the treatment of primary multiple sclerosis-related fatigue (PMSF).  MCT-125 has demonstrated efficacy in a 138 patient Phase IIa clinical trial for the treatment of PMSF.

MCT-485 is cytotoxic agent, and the first in a family of cancer therapeutic candidates based on MultiCell’s TLR3 signalling technology.  The candidate acts by directly inducing tumour cell death, and triggering production of TNF-alpha by cancer cells, amplifying  both apoptotic effects and triggering a localized immune reaction that has the potential to generalize and curb progression of metastatic cancer, the firm claims.

MCT-485, along with MCT-465 and MCT-475 and are based on MultiCell’s therapeutic antibody and synthetic dsRNA technologies. The immune enhancer MCT-465  is indicated as an cancer adjuvant therapy alone or combined with currently available or novel therapies. MCT-465 can also be used with our MCT-475 antibody therapeutic for the treatment of cancers including breast carcinoma. MCT-485  is a novel synthetic dsRNA with therapeutic properties distinct from those of MCT-465, and is indicated for the treatment of certain cancers. MCT-465 is in early-stage preclinical development, and MCT-475 and MCT-485 are in the discovery optimization stage of development.

High-Fidelity PCR Reagents Continue to Propel DNA Research with Unparalleled Accuracy and Speed

New proprietary tracking dyes within high-fidelity DNA polymerase and qPCR master mixes facilitate easier, more accurate PCR set-up for researchers.  The Thermo Scientific Phusion Green High-Fidelity DNA Polymerase and Luminaris Color qPCR master mixes utilize proprietary tracking dyes to speed up the pipetting process. By providing a visual confirmation, the mixes are designed to vastly reduce the chance of human error, while also decreasing the number of procedural steps required. Most importantly, the dyes have been engineered not to interfere with the reaction or any downstream applications.

These new Phusion and Luminaris products form the latest additions to the renowned Thermo Scientific Phusion High-Fidelity DNA Polymerase family. First launched a decade ago, the Phusion High-Fidelity DNA Polymerase was the first DNA polymerase, developed using fusion protein technology to provide a combination of accuracy and speed previously unattainable using conventional enzymes.  Known for performance with all major PCR applications, the Phusion technology has played a central role in helping researchers accomplish an array of key scientific achievements, including the creation of the first functional synthetic genome.

“Phusion Polymerase technology has dramatically increased the productivity of labs running PCR,” says Margarita Leckiene, Director of Nucleic Acid Detection & Molecular Tools for Thermo Fisher Scientific. “Our philosophy is to simplify customer workflows further and increase the reproducibility of results. We are constantly working to further advance the accuracy and robustness of Phusion polymerase including the most recent innovation of special enzyme formulations that are optimized for NGS applications, offering equal amplification efficiency across entire genomes.”

This technology, coupled with Thermo Scientific PCR instruments and accessories such as theThermo Scientific Arktik Thermal Cycler and Thermo Scientific Piko Plate Illuminator, provides an efficient integrated solution that delivers

• DNA amplification with extreme accuracy
• Enhanced visual control
• Significantly shorter protocol times.

To recognize the impact of the original Phusion polymerase and a subsequent decade of innovation in this area, Thermo Fisher has launched Phusion Fest, an interactive campaign celebrating the importance of PCR in the laboratory. Phusion Fest encompasses a fun, scientific trivia game, special offers across a variety of molecular biology products and free samples of selected Phusion products.  More information on Phusion Polymerases and the Phusion Fest can be found by visitingwww.thermoscientific.com/phusion.

The Future of Drug Discovery: Two Required Supplements to Current Practices

A massive restructuring is currently taking place within pharmaceutical industry drug discovery sector.  Consequently, we suggest that phenotypic screening and drug repositioning will need to be increasingly utilized to uncover new therapeutics.

A convergence of forces in the pharmaceutical/biopharmaceutical industry continues to drive rapid rates of business restructuring in 2013. Although US regulatory policy issues, healthcare costs, globalization and other forces all contribute to this morphogenesis, the patent cliff that we are working through now has been a tremendous contributor as roughly $100 billion in sales have or will go off patent from 2010 to 2014. This massive loss in pharmaceutical companies’ top line is naturally flowing down to an array of cost-cutting measures in the industry, but drug discovery R&D is the true “canary in the coal mine.”

Since 2000, the pharmaceutical industry has eliminated about 300,000 jobs — as many people as currently work at the three largest drug makers (Pfizer, Merck and GSK) — combined.1 A significant portion of these are chemists, biologists and other scientists who participated in drug discovery. Large portions of R&D process have been outsourced to third-party, offshore contractors, raising criticism that true innovation, vital for drug discovery, is being significantly compromised.2 And although some have argued that drug discovery will increasingly shift into small to mid-sized biotechnology companies, and even academia, these sectors are also reeling from disruptions in the venture capital markets and pressures on the federal budget.

Phenotypic Screening

These disruptions and the restructuring of drug discovery across the industry have not significantly affected later stage, clinical programmes, as observed in rates of new drug application (NDA) fillings and new drug approvals, which have remained relatively stable (and even improved) in the last few years. It seems, however, quite likely that a day of reckoning is lurking on the horizon when opportunities for new drug candidates may begin to dry up. As a consequence, the industry has become very pragmatic about cost-effective strategies to drug discovery. Our experience indicates that strategic approaches such as phenotypic screening and drug repositioning will continue to be increasingly adopted in this new cost-effectiveness driven era of drug discovery.

The first of these strategies, phenotypic screening, can be viewed either as a departure or a complement to our existing standard paradigm for drug discovery — the target-based medicinal chemistry approach that runs central to all pharma R&D operations and has done since the 1980s and 1990s. The current approach, in a reductionist way of thinking, relies on our current understanding about biochemical pathways and their relationship to disease processes.

Literature Occurrences of Phenotypic Screening

Hypotheses are developed as to what enzyme or receptor should be modulated (inhibited or activated) to effect a positive outcome on a disease process. By contrast, phenotypic screening is, by its nature, not hypothesis-based, but instead is an empirical approach that relies upon observations of drug candidate activity in a system, such as an animal model of a disease process, independent of any initial hypothesis of why or how that candidate may be therapeutic towards the disease. One can make the argument, as have many pundits who have commented on the productivity gap in the pharmaceutical industry, that given the extreme complexity of biochemical pathways within intact higher organisms, that our hypotheses are too often wrong and the cost of testing them is too expensive leading to higher and higher R&D investments without concomitant productivity.3–5

The seminal publication by Swinney and Anthony showed, that despite an industry essentially focused on new drug discovery using the target-based approach, most first-in-class small molecule drugs were discovered by phenotypic screening.6 A possible criticism of phenotypic screening is that it will discover off-target effects that then need to be followed-up through additional hypothesis-based research. Based on our own mechanism of agnostic phenotypic screening of more than 200 drugs, we strongly believe that most (75–90%) of new biology uncovered by phenotypic screening is driven by on-target effects.7 An unbiased phenotypic screen is far more likely to uncover unexpected biology for a known mechanism than it is to discover new biology because of an off-target effect. Some companies such as Eli Lilly have been pursuing phenotypic drugs for close to a decade as a complement to mechanism/target-based drug discovery. Nevertheless, in our opinion, there is considerable opportunity to incorporate an increased balance of phenotypic to mechanistic screening in drug discovery. The barriers to increased phenotypic screening are partially the cultural familiarity with the mechanistic approach and in the case of cell-based assays the technical challenges of incorporating high-density data readouts into higher throughput assays.

Drug Repositioning

The second strategic element that is increasingly being adopted to increase cost-effective drug discovery is drug repositioning. It turns out that the famous words of Sir James Black: “The most fruitful basis for the discovery of a new drug is to start with an old drug” has a sound biological rationale that Black could not have fully appreciated at the time. From the point of view of molecular evolution, we now know that nature ‘recycles’ protein motifs again and again, and for this reason the chemical universe of biologically active compounds is characterized by dense spaces of hotspots with vast amounts of chemistry space that is biologically empty.8

Literature Occurences of Drug Repositioning

Although this finding is useful for medicinal chemists seeking to design new chemical entities, it also has a corollary, namely that compounds designed for one therapeutic area often have therapeutic benefit in other areas. In fact, it turns out that about 30% of approved drugs are labelled for indications other than the indication for which they were originally developed.9  Moreover a Thomson-Reuters Integrity database analysis shows that a drug in development for a single indication is the exception and that pursuit of multiple indications is the rule.10

Identifying new drug candidates from within the existing pharmacopeia (drug repositioning) has the well-recognized benefits of short cutting development, and thereby significantly reducing costs, by virtue of utilizing pre-existing preclinical and clinical drug development data. In most cases the first clinical studies for a new candidate can be in disease patients thereby obviating the time, expense and risk associated with Phase I studies. Although composition-of-matter patents typically are unavailable to provide exclusivity for repositioned candidates chosen from previously studied drugs, method-of-use patents, in many contexts, can be as rigorous (for compounds that have never had market approval). Also, with the lower costs to market it can be the case with many therapeutic product opportunities that the required commercialization thresholds may be met with the 5 years of data exclusivity available in the US and 10 years available in Europe through Trade-Related Aspects of Intellectual Property Rights (TRIPs) (again applies to compounds that have never had market approval).11 At a recent Washington DC drug repositioning conference there was audience unanimity that increasing the data exclusivity period was the single event most likely to enhance the drug repositioning field.

Process Validation Guidance: A Bad Fit for Aseptic Processing?

When he first assessed FDA’s draft Process Validation guidance a few years ago, consultant James Agalloco, president of Agalloco & Associates, saw the usefulness of the guidance for validating pharma production processes and products. “The life-cycle model will result in development and validation exercises that provide relevant and meaningful information,” he wrote. “The link between the process parameters that influence the critical quality attributes will serve the industry well. The use of statistical methods will add a rigor to the validation efforts that has been sorely lacking” [1].

However, he expressed serious reservations (and shared them with FDA) about whether the PV guidance could easily be applied to processes and systems “less clearly related to end-product quality attributes.” This includes sterilization and aseptic processes.

“There are simply too many independent—and interrelated—variables in aseptic processing, and the most meaningful one of all lacks metrics of any type . . . Aseptic processing performed by human operators is devoid of any measurable variable that could be used to predict the outcome.” He concluded: “The statistical component of the guidance really doesn't work with respect to linking any process parameters directly to performance.”

Now that the guidance is official, we checked in with Agalloco to see if his views have softened. Far from it, it turns out. The guidance is a “terrible fit” for the validation of aseptic processes, he maintains.

“I've seen more confusion than clarity,” Agalloco says. “I've seen no indication in the final guidance or anywhere else that suggests things should be changed to accommodate sterility within the new guidance.”

Agalloco teaches regular courses on validation of aseptic processes, but has not changed them to reflect the new guidance. “There's nothing you can change in the practice of either sterilization or aseptic processing that is of any value to fit the guidance,” he says. “I see no need to adapt, because the adaptation will gain nothing of real value.”

The new guidance reinforces the need for science and process simulation testing—isn’t that a good thing? In general, yes, he says. But, “for aseptic processing its about set-up and interventions, and what the operator does in relation to them. Humans are awful subjects for DoE, QbD and all of the scientific buzz we hear about in relation to the guidance.”

Will the PV guidance dramatically change the way manufacturers approach the validation of sterilization processes—filtration sterilization, for example?

“Not in the least,” Agalloco says. “There's no suggestion that anything can or should be changed to match the guidance. Even FDA (Grace McNally, at PDA in San Antonio in April of this year) has stated that the ‘guidance doesn't specifically apply to sterilization.’ Making changes to fit these processes to the guidance is done at one's peril.”

The PV guidance recommends activities in three stages (process design, process qualification, and continued process verification), which depend on process characterization studies (with Key Process Input Variables, Design Space, etc.). Are most manufacturers able to adequately characterize their aseptic processes and sources of variability?

Says Agalloco: “Not at all. As the article [1] states, there's some possibility with sterilization as it relates to Stage 1, but that's about all that fits. Stage 2 & 3 lack adequate metrics to develop the confidence from what we can routinely measure in the process for sterilization so it's not a good fit there. Aseptic processing is just a horrible fit all around.”

 

We also consulted with Sartorius Stedim’s Maik Jornitz, current chair of the board at PDA, about whether the guidance will make a difference regarding the validation of filtration processes, one of his areas of expertise. “I do not think that the new Process Validation Guidance will change the process validation needs and activities surrounding sterilizing grade filtration,” he says. “The 2004 Aseptic Processing Guidance makes it very clear what is expected by regulators in regard to sterilizing grade filter validation, which is still very valid and unchanged by the new Process Validation Guidance.”

Will this guidance help filter end users to better understand which filter process input variables are "critical to quality"? “I do not know whether it will help, but it will raise the awareness and emphasis that there are critical variables within the filtration process,” Jornitz says. “The 2004 Aseptic Guideline touches upon and PDA Technical Report #26 describes in detail these critical process parameters, if the awareness is not already there.”

Jornitz, a frequent writer, speaker, and lecturer, has long preached a “science-based” approach to filter validation, and so believes the PV guidance will support this trend but is not necessarily a game-changer: “I believe everybody in the industry is working in accordance to a science-based approach,” he adds. “Therefore, guidances are only there to reiterate or support what should and is commonly done—or so I hope . . . I do not think that the Process Validation Guidance states something new here.” 

Green chemistry has ecological, financial benefits for Pfizer

When people hear the word "green" these days, they automatically think "environmentally friendly." But among chemists at Pfizer Inc. in Groton, the word also denotes efficiency, which equals a different type of "green" - money.

And Pfizer's local laboratories have been saving the company plenty of money - millions of dollars, most likely, though the company won't provide specific figures - over the past few years through a relatively new idea called green chemistry. Scientists in Groton are constantly reviewing and revising the chemical processes that go into the manufacture of top-selling medicines such as the cholesterol blockbuster Lipitor and pain reliever Lyrica, making sure new drugs are produced in the most efficient manner possible.

"Pfizer is a leader in both the research and implementation of green-chemistry and green-engineering practices," said S. Stewart Slater, a professor of chemical engineering at Rowan University in Glassboro, N.J.

By going green - which local scientists spearheaded at Pfizer a decade ago, though the chemistry principles date to the early 1990s - the making of pharmaceuticals is being done in a less wasteful, safer and more benign manner.

"By being green chemists, I think we provide a particular benefit to the environment," said John Wong, senior research fellow at Pfizer's Groton labs and leader of the eight-member Green Chemistry Team there. "When you use enzymes to do chemistry, they are not toxic and certainly environmentally friendly."

In the pharmaceutical industry, green chemistry often means the replacement of organic solvents with enzymes, commonly referred to as "nature's catalysts" and naturally occurring in all living organisms. Companies try to implement green chemistry ideas right from the start, because changing drug formulations after a product is out requires additional human testing for safety and effectiveness.

Eric Watters, environmental manager of the Groton facility and a team member, said the use of green-chemistry methods doesn't have a big impact on the local air and water because the pharmaceutical giant no longer has extensive manufacturing facilities here. It's at the company's drug-making plants worldwide that the use of green chemistry is most noticeable on the environment, he said.

The environmental impact is felt most profoundly with reductions in the amount of carbon dioxide released into the atmosphere. Efficiencies and cost savings come largely from reduced use of raw materials and significant cuts in energy use.

To give an example, Wong pointed to a new process for manufacturing Lyrica developed by his team in Groton that reduced carbon-dioxide emissions by 43 percent using one of Pfizer's measurement tools. The company expects a further reduction in emissions of 20 percent as it continues to refine the method through the end of next year, he said.

Avoiding chemical waste

"The pharmaceutical sector has embraced green chemistry most enthusiastically, perhaps because it has the most to gain," according to an article last month in Nature News. "Pharmaceutical plants typically generate 25 to 100 kilograms of waste per kilogram of product, a ratio known as the environmental factor, or 'E-factor.' So there is plenty of room to increase efficiency - and cut costs."

The company expects that green-chemistry processes used to reimagine the production of Lyrica will, over a 13-year period, avoid about 200,000 metric tons of organic chemical waste. Pfizer scientist Peter Dunn, who in 2006 became the pharmaceutical industry's first full-time green chemistry leader, has said the rejiggering of three product lines alone saved the company the cost of 500,000 metric tons of chemicals.

According to Pfizer spokeswoman Sperry Mylott, the company's drugs currently in late-stage development use 24 percent less solvent per kilogram than the most advanced compounds it was testing a few years ago, "thus achieving one of Groton's chemical R&D team's environmental goals two years ahead of schedule."

Chemists do small-scale experiments in Groton before trying out their ideas on a bigger stage with Pfizer's manufacturing partners, said local team leader Wong. The idea is to be as "atom economical" as possible, he added, meaning that less material used up front leads to less waste in the end.

"The company is quite good at implementing process improvements," he said.

Although the pharmaceutical industry in general did not embrace the principles of green chemistry right away - chemical companies faced with outcries after the Love Canal fiasco and the Bhopal disaster had a greater incentive - it now is more motivated, as drug discovery has waned and cost-cutting is getting more attention.

As far back as 1998, Pfizer scientists in Sandwich, England, had worked to improve efficiencies in the manufacture of Viagra, which at that point produced 105 kilograms of waste for every kilogram of product. Pfizer eventually reduced the E-factor to 8, meaning the production of Viagra became more than 90 percent more efficient.

"Ultimately, it's all economics that drives it," said Connecticut College chemistry chair Marc Zimmer. "You can't just let waste go down the drain anymore. You have to dispose of it, and that means you have to pay for it."

Improving production methods of antidepressant Zoloft as well as Viagra and Lyrica have won Pfizer major green chemistry awards. The 2002 Zoloft green chemistry project, conducted at the Groton labs, won the U.S. Environmental Protection Agency's Presidential Green Chemistry Award.

Early stage development

The local labs also have their own internal awards, and winners may designate the prize to an educational institution. In the past year, the prize went to Pfizer scientist Jamison Tuttle, who designated that the $5,000 award be given to his former Connecticut College professor Timo Ovaska, who in turn plans to use it for research stipends for summer students.

"Green chemistry is still in an early stage," Ovaska said, "but it's definitely having more and more of an impact."

Wong said Pfizer doesn't force green chemistry on anyone, but there is a constant effort to educate employees in the science. He added that chemists have been quick to embrace the concept and are always brainstorming and experimenting with new ideas for making pharmaceutical production less costly and easier on the environment.

"It's part of our day-to-day activities," he said.

Ordinary Measures : Everything I needed to know I learned in Freshman Chemistry 101

For Years, pharma has been the villain in most public opinion polls. But now, the tide seems to be turning. Last month brought buzz about the film, Extraordinary Measures. You’ve no doubt heard all about the inspiring story of John Crowley, a pharmaceutical exec who quit his job to fund research into cures for Pompe disease. Not only did he save the lives of his own, and many other, children, he opened up the field of orphan drugs.

Although critics are lukewarm, they say the movie does reveal to the public more of the complexity and challenge of drug development and manufacturing. What could be better than that? But lately I’ve been wondering about the “other” side of manufacturing—the side that could never make it to Hollywood: cGMPs and quality control. Drug recalls in the U.S. have been trending upwards. In the U.K., according to a recent study by Blueview Group, drug and medical device recalls increased 400% between 2004 and 2008, due mainly to manufacturing defects, packaging or labeling issues, or compromised sterility. 

Pharmaceutical quality control reached a climax in the news last summer, after FDA issued Genzyme a 483 for cGMP problems at its Allston Landing plant. This plant manufactures the orphan drugs Cerezyme and Fabrazyme, worth nearly $2 billion in sales each year. A dissident shareholder, Relational Investors, sued the company, its principal alleging that Genzyme overpaid for acquisitions and underinvested in manufacturing. The problems cost Genzyme dearly, as FDA did not approve its improved Pompe disease treatment (which is now being re-evaluated) and reportedly streamlined the approval process for a competitor’s product. Last month, Genzyme hired a new QC chief, and contracted with Hospira to handle filling. Relational withdrew its suit, and hopes to settle its diff erences with the company.

But the question still lingers. Is the industry, in its desperate attempt to acquire innovation and reinvent itself, underinvesting in core quality control operations? Last month saw another major pharma company, J&J, dogged by quality control issues. Th e company has led the industry in Lean Six Sigma and operational excellence initiatives, so this news may have surprised some. But J&J had to recall more lots of Tylenol and other over-thecounter medications, which were tainted with a chemical used to treat wooden pallets. FDA alleged that J&J was aware of the problem a full year before it took action.

It’s easy to point the finger at senior management. But is that the whole story? Is everyone on your team being rigorous about quality? We often lament about pharma’s silos and its “data rich, information poor” problem. But that doesn’t mean that each and every critical data point shouldn’t be recorded, transferred and shared. Last month, we interviewed experts on the topic of tech transfer, and found that people oft en fail to transfer basic information to internal partners, or external CMOs. Information is missing, isn’t recorded, and, in deals involving China and Japan, isn’t translated. And analytical methods and SOPs are the areas where people most often trip up. This has an obvious impact on CAPA and fundamental quality control.

Contributing editor and NIR expert Emil Ciurczak suggests that some have “gotten lazy and stupid” about documenting critical details. “Everything you need to know to pass an FDA inspection you learned in freshman chemistry class,” he says. One industry consultant recalls a consent decree in the 1980s, where a lab technician openly admitted to FDA to taking notes down in pencil, then changing them to ink later on. She also responded to questions about an SOP. “Nobody does it that way,” she said. “Everyone knows it won’t work that way.”

“How could anyone have hired such an incompetent tech?” you ask. But is it really that farfetched? Read any 483’s lately? It’s the ordinary measures, as well as the extraordinary ones, that count.

Biotech Production: Planning, Scheduling and Throughput Analysis with a Combined Theory of Constraints, Lean and Simulation Approach.

A major biotech company’s only final stage bio-manufacturing facility in the world was struggling to meet rapidly increasing customer demand. Unanticipated production delays and frequent starvations at critical parts of the operation were causing not only late and missed deliveries, but also the expiration of product batches at a cost of approximately $1 million per batch.

They had not been able to identify the root cause of their production delays, nor find a suitable solution. As a result, they were planning to invest $1.2 billion to add another 500,000 square feet to the existing biotechnology bulk manufacturing facility.  Before proceeding with a capital investment this large, it made sense for the team to make sure they had considered all other feasible solutions first and/or at the very least validated their assumptions leading them to the conclusion of needing additional space. They engaged ProModel Corporation to help. 

We recognized that this project would require a discrete-event simulation solution along with aspects of Lean and TOC (Theory of Constraints).  Lean is commonly applied in manufacturing in order to reduce and eliminate waste, TOC is used to help discover which issues are limiting the overall system performance and Simulation is used when the variability and interdependencies of a process or system are such that traditional problem solving approaches are too risky or inaccurate.

This article discusses how using a combined lean and simulation approach helped one pharmaceutical manufacturer produce two more lots per month resulting in additional revenue of $25 million/month as well as eliminating expired batches at a savings of $1 million/batch, without expanding the facility at a projected cost of $1.2 billion.

Due to a confidentiality agreement with the manufacturer we can not disclose their name, but we provide this case study and best practices article as awareness and exposure to the idea of using simulation in combination with Lean and TOC to solve complex manufacturing throughput and scheduling issues.

We worked together with the client’s project team using TOC, Lean and Simulation to do the following:

• TOC to discover the bottlenecks
• Lean principals as a way to develop the ideas and concepts that could help resolve the problems
• Simulation as a way to test and analyze these ideas and determine precisely how to implement the best ones, all without risking any interruption to the on-going production process. 
• An additional advantage of a simulation solution is that most of the time the organization also gains a problem solving tool and capability that can be used over and over again to solve and even prevent similar future problems.

ProModel’s approach is an iterative three phase process called VAO (Visualize, Analyze, Optimize). The remainder of this article will summarize how Lean and Simulation were used synergistically within this approach to achieve the client’s objectives. Within each of the VAO sections, there will be some best practices listed at the beginning, and then some specific examples of how those practices were used in this particular project.

The specific client objectives for this project were to:

• Uncover the root cause(s) of the unanticipated delays creating the late and missed deliveries.
• Develop a repeatable, accurate predictive tool that allows them to analyze and identify, in a risk free    environment, which potential changes will eliminate the current delays and help prevent future ones.
• Determine if, when, and how much additional capacity from a new facility would be required.

VISUALIZE Phase – Understand Your Current State Environment

Best Practices

The visualize phase helps the organization better understand their actual current operating state and sets up the entire project correctly in several ways:

• Ensures the right problem or issues are being solved. This is done by starting with the end in mind; identifying the actual results that need to be achieved, and then what type of output information the simulation model must produce in order to help make decisions on the appropriate process or policy changes.
• Helps the company truly understand how their current environment is actually operating through building, validating and verifying a current state model, not just how they think it’s operating.
• Often organizations gain a great deal of value from simply going through the process of gathering data and putting together VSM (Value Stream Maps) or Process Flow Charts without ever simulating.  If the maps exist, then the simulation step helps them to take advantage of work they have already done. 
• Building and validating the current state simulation model can then further enlighten an organization without even testing the first new idea by identifying through sensitivity analysis which processes or resources are really critical (bottlenecks) to meeting their performance goals so that you only spend time on improving the areas that really matter
• Builds trust in the simulation concept by allowing those involved to visualize the current operation and say “yes this is how things really do work around here”.This paves the way for the optimize phase so that when changes are proposed, there will be more confidence in accepting the results predicted by the model from implementing the changes.
• Provides a measurement of the gap between current and required performance

Project Implementation

In this case, starting with the end in mind, the project team determined that the model had to replicate not only the manufacturing process itself, but also the production planning/scheduling aspect of the current system as well. This necessitated taking into account parameters such as: workforce resources, process flow, inventory levels, availability and movement, equipment, scheduling, and product mix. The simulation output information generated had to enable the Production Planning Team to test schedules, visualize the impact on throughput and cycle time when changes are made to these parameters, and provide the optimum schedule to the Production Execution Team.

Therefore it was determined that value stream maps/process flow charts were needed for their manufacturing system including inventory policies and production planning/scheduling methods. Microsoft Visio was used to create the VSM’s and ProModel’s Process Simulator, a plug-in to Visio was used to convert VSM’s into simulation models. (below)

To develop this model it was necessary to collect current and historical data on the production operation and use that as a base line for setting up and validating the simulation model.

The VSM’s would serve as the virtual representation of the production process for the animated simulation runs. Customizable ProModel compatible Excel input templates were also designed within the tool to enable the team with the capability of changing model data for rapid “what-if” scenarios.  The model was then integrated with scheduling software. After running each scenario, the team was then provided with Gantt charts that allowed them to examine actual planned schedule performance.

Summary of Steps to Develop Model

• Microsoft Visio was used to map out the process flow and used as the virtual foundation for the model. If the analysts want to change or add a new process they simply change the flow chart and it automatically updates the model and the Microsoft Excel input templates.
• Excel input templates made it easy to change production tasks, process times, and the resource requirements.
• Scheduling software was integrated with the model and was used to evaluate the proposed schedules with Gantt chart output reports.
• Easy to use Design of Experiment capabilities in concert with a set of user definable Key Performance Indicators provided the method to rapidly evaluate system performance across an unlimited number of scenarios.

Working with the client project team it took about 8 weeks to create and validate an integrated production simulation and scheduling model.   After the model was built and historical data used to validate and verify that the existing model was with an acceptable range of accuracy of the real system, usually 95% or better, the next step was to begin analyzing the situation.

ANALYZE Phase – Identify Root Cause(s) and Brainstorm Potential Changes Using Lean Concepts

During the analyze phase the model is used to help identify root cause(s) of performance issues as well as to brainstorm ideas and create scenarios that might improve the processes to meet business objectives

Best Practices

Before beginning to test anything, utilize the current state model to view the animation and generate output reports that allow analysts to diagnose what issues are keeping them from operating up to the designed system performance levels. Lean concepts and thinking can then be used to develop a list of feasible ideas to test in order to improve performance based on truly knowing what is wrong.   It’s tempting to want to test everything because experimenting with the model can be “cool”.  However, from a time, cost and reality standpoint, you can’t test everything so it makes sense to test only what Management would actually consider implementing.

Once a feasible list of ideas was generated and prioritized typically the model needs to be enhanced to incorporate the actual changes to be tested.  Depending upon which changes are to be tested the model can be modified to include just the first test or all of the tests; this has to be determined on a case by case basis.

Lean methodology prescribes having a Kaizen Event in order to implement changes, where the changes are implemented based on value stream map analysis and the suggestions of the people doing the work at a particular station or process.  This is a good idea on much of the low hanging fruit.  However, when proposed changes are too risky or too complex to test with the traditional Lean Kaizen Event method, simulation is a more effective tool to conduct “Virtual” Kaizen Events.

Project Implementation

On this project, they used the validated model of their current state to observe the animated representation of the system as well as several types of output reports to help identify their underlying performance issues as follows:

1) The key bottlenecks or pacing processes changed radically depending on the product mix. If the product mix changed enough, an item that was on the critical path and crucial to delivery one month may not be on the critical path the next month. 

2) With a schedule and inventory based on the constant manufacturer driven PUSH system, the client couldn’t respond fast enough to the unstable changes in demand, which led to their debilitating system constraints.

The manufacturer’s Industrial Engineering (IE) team had previously looked at many aspects of their production system as possible causes for the inadequate throughput and subsequent batch expirations. However, until beginning this project with ProModel they had not seriously considered possible cause or solutions outside of the traditional plant floor improvements such as additional equipment or labor, or additional space as discussed earlier. This time around though, their IE team, working with us under the VAO approach, considered the entire production environment not just the plant floor manufacturing processes.

As a result, while collecting information and developing the current state model, the Industrial Engineering Team discovered that Production Planning was scheduling using a PUSH system. The team thought that changing to a PULL/JIT (Just-In-Time) system might help reduce the delays, which made sense considering the root cause discovered. This is very much a lean concept, because normally changing from a push to pull system helps to reduce waste such as excess inventory, prevent scrap, damage, and rework and obsolescence, such as the expiring batches that this manufacturer was experiencing. The team decided to pursue this alternative as the first option to test with the model.

Changing to a pull strategy required answers to the following questions before implementation:

• What levels of inventory should be used to ensure line continuance, but not result in lost batches due to expiration?
• If/when would additional labor be required?
• When in the future, if at all, would additional line capacity be required?
• More Info required here

Once the decision was made to analyze a Pull strategy, some additional functionality had to be added to the model including:

• User friendly input settings were implemented to adjust varying levels of inventory controls by sub-process and the different lot types which included vial, syringe, and drug type. 
• Provide Customized Input Templates to enable the team with the capability of changing model inputs for rapid “what-if” scenarios.
• Provide a Gantt Chart Report (figure 5.jpg) to enable team with the capability to examine actual planned schedule performance including the output of supporting services requirements
• Develop Scenario Runner interface for rapid “what-if” analysis of process improvement suggestions.
• Provide an iterative planning correction procedure to enable the team with the capability to examine the performance of the system and make iterative changes to improve and correct planning issues.

After updating the base model to be representative of a pull strategy, and verifying that it works correctly, the team moved on to the Optimize phase in which it would evaluate in detail if this new approach works, if so, what are the operating parameters, scheduling rules, inventory levels and policies that must be implemented in order to reach maximum capacity.

OPTIMIZE – Design the Future State Operating Environment

During the optimize phase, the model is utilized to test the ideas generated in the Analyze, in order to determine the optimum set of changes to make in order to reach the planned objectives.

Best Practices

When starting to use a simulation model to test ideas, scenarios are usually created and run in a particular sequence to help get at a result in the most efficient manner:

1) Run the “Current State” scenario in order to have baseline results available

2) Design and run scenarios from a high level, with only the level of detail/resolution needed down to lower level details.  Running scenarios with more resolution than needed makes finding the optimum solution more difficult than it needs to be.   

3) Test only the changes at the bottleneck or constraint to start with.  Until the bottleneck/constraint situation is resolved, testing other changes will not necessarily be meaningful.

4) Run additional scenarios with increasing breadth across the system and/or increasing levels of detail and resolution as needed until the optimum solution set is found.  This is the new future state environment.

5) Test the robustness of the system by varying input parameters to push the boundaries in order to see how much performance is affected when things don’t go according to plan. 

6) Finally, repeat steps 2-6 in an iterative fashion as required if the first strategy does not produce the desired improvement.

Project Implementation

Using the best practices steps above, the team utilized the model to run scenarios around schedules and inventory levels in order to predict and quantify the realistic throughput performance of their proposed PULL system.  Key Performance Indicators were available after each simulation run in the software output reports module.  These output deliverables included: Overall Cycle Time, System Throughput, Work-in-process levels and Resource Utilization. From each of these data charts the IE team was able to compare scenario results and refine each simulation run until the optimal integrated production schedule was discovered.

Some of the key items tested in modeling the pull system were as follows:

• Starting the precursor activities to the formulation and filling processes 5 days before the production date to allow for needed equipment or subassemblies to be manufactured.  
• The start date and time of the formulation and filling lots.
• The time lag between input start and formulation or filling lot start were controlled by delay tasks that appeared as inputs in the task controls tab of the input spreadsheet. 
• Both planned and unplanned downtimes affecting the schedule were modeled in a similar manner, using two pieces of information: the frequency and duration of each downtime.
• Inventory levels
• Product mix and batch/lot sizes

The solution confirmed their idea that the production environment had to be converted to a PULL system in order to meet the required customer demand and still have enough capacity to absorb predicted variation.  Additionally it took less than 8 weeks for the project team to arrive at this optimal solution and immediately they made the necessary changes to convert to a Lean, demand driven, PULL system.  

If optimize the model performance of the future state system with the PULL strategy would not have been sufficient, then the next iteration of Analyze and Optimize would have started, and a second idea or  strategy would have to be tested in addition to or in place of the PULL concept.

RESULTS

The simulation solution allowed them to define the right combination of scheduling sequence, product mix, batch size, in-process inventory and process changes that enabled them to meet customer demand virtually eliminated line starvations, batch expirations, and late/missed deliveries.

The client project team, working together with ProModel and the solution above, were able to accomplish the following:

• Determined how, the facility could produce two more lots per month, which has resulted in a monthly revenue increase of over $25 million.
• Defined the right combination of in-process inventory levels and process changes (going to a pull system) that would virtually eliminate batch expirations, which resulted in cost avoidance of over $3 million per year.
• Developed the capability to quickly schedule around the impact of significant unplanned downtimes.
• Provided the ability to optimize labor use and eliminated the need for additional staff.
• Provided a better way to do long-term expansion planning and predict when and where more line capacity would be required, thus eliminating the immediate need to spend $1.2 billion on additional manufacturing space.

DELIVERABLES

Deliverables to the customer for this project included the following:

• Recommendations arrived at with their IE and Production teams to convert to a pull system with precise metrics and specific process changes in order to improve performance to desired levels
• Repeatable strategic and tactical planning capability through the Customized Flexible, Reusable Predictive Modeling Application
• Technology Transfer Training and documentation to provide their team with a detailed understanding of the solution construction, input templates, Scenario Runner and output reports so they can continue using the model to plan and optimize the process into the future.

CONCLUSION

Best Practices

We have found the following four steps are used by our most successful clients in order to engrain simulation into the culture of the organization.

1) Build a model to answer the current questions with available data, don’t dive too deep right away.

2) Refine and expand the model as needed to answer tomorrow’s questions.

3) Build up internal core competency over time by developing two or three expert model builders and many experienced in using the models built for them. Organizations can build and benefit most of their own simple to medium complexity models this way

4) Use a simulation consultant for very complex situations.

Project Implementation

In this case simulation proved to be a powerful tool.  In general it can be used alone or along with other methodologies such as Lean and Theory of Constraints in order to help reduce the risk and improve the speed and accuracy with which an organization can do the following:

1) Perform root cause analysis on underperforming processes and systems.

2) Provide an objective sand box like environment in which to test new ideas, strategies, and policies in order to determine the most effective courses of action for each individual organization.

3) Gain an organizational capability for predictive, preventative performance planning and process improvement.

This particular Biotech Company now has a capability to constantly re-examine the performance of their system and make the changes needed to improve and correct planning issues before they occur and create significant problems.  By predictively analyzing their production planning, scheduling and throughput scenarios in a no risk simulated environment, they will continually make better decisions faster.