Editor’s note: This is the second article in a series following Ragu Bharadwaj’s progress through the System Design and Management Program. Bharadwaj, a computational chemist, previously discussed areas within the pharmaceutical industry that might be improved through systems thinking. In this article, Bharadwaj reveals what he’s learned from SDM so far.
In just one SDM semester, I have been exposed to a mountain of ideas that I am still processing. Already I’m impressed by the ways in which systems thinking could transform and improve the pharmaceutical industry.
SDM looks at the big picture—what is a business’s ecosystem and why do companies fail? But it also goes to the root of problems that pharma and other industries confront every day—for example, what’s the best way to compare risks and benefits for optimal decision-making? Through it all, SDM maintains its focus on the people skills needed to make any business succeed.
SDM began with the month-long January program, affectionately known as SDM “boot camp,” which gave my leadership and management skills a workout. Students under take two design challenges while also attending lectures on ethics, leadership, public speaking, and negotiation. Lessons are immediately put to use on assigned teams, as tight deadlines and a heavy workload drive home the importance of using each person’s unique skills and experience.
The spring semester armed me with SDM tools: engineering risk benefit analysis (ERBA), marketing, and technology strategy. ERBA and decision analysis are of paramount import to the pharmaceutical industry because projects have such long lead times. Scientists often make decisions based on gut instinct when even a rudimentary risk-benefit analysis would help. I’ve been involved with several biotech startups where the risk of decisions was tacitly known but never quantified. What I’ve discovered through SDM is that even when there is great uncertainty, quantification stimulates the discussion of risk and inspires contingency planning.
The pharmaceutical industry could use SDM tools to improve laboratory design, high-throughput screening, company strategy, and disease-fighting strategy. In laboratory design, the mean time between failures and criticality can be used to make decisions on how much redundancy to allocate for equipment, employee resources, and suppliers.
High-throughput screening (HTS) involves screening available libraries of up to a million compounds for potentially active compounds (“leads”) to follow up for development. HTS yields good starting points but is expensive, and results depend on factors like assay quality and selection method. Quantifying the probability of missing leads because of errors should improve the usefulness of HTS.
On a larger scale, ERBA should help companies make better strategic decisions about which projects to pursue and which diseases to target. The tools taught in SDM could help answer key questions, such as:
•Given a fixed budget, would developing treatments against multiple mechanisms in a single disease area have a greater chance of success than developing treatments for different diseases via a single mechanism?
•For a single disease area, would attacking the same mechanism with different compound scaffolds be more successful than attacking different mechanisms?
These kinds of questions are vital to a company’s very survival—a point driven home in SDM’s technology strategy course. This is an absolute must for those interested in product strategy or business development in pharma. While such analyses are sometimes used in large pharmaceutical companies, they have yet to seep into biotech startups.
I know many a startup that could have been saved by this kind of analysis. I hope more industry errors can be avoided as SDM tools begin to spread throughout the industry.