Issue link: https://www.e-digitaleditions.com/i/1173734
20 October 2019 Tablets & Capsules Putting the cart before the horse One of pharma's greatest shortcomings as an indus- try has been its tendency to focus on the wrong things. We saw this with process analytical technology (PAT), where the industry focused on the design and imple- mentation of the technology and ignored the impact of foundational material characterization and supplier con- trol. We also saw it with lean and six sigma, where the emphasis on the tools and certifications, in the absence of the cultural leadership components, relegated these operational excellence philosophies to simply a suite of tools rather than a holistic approach to business perfor- mance. Pharma 4.0 has the potential to fall into the same trap. The focus on technology in the absence of under- standing the basic question to be answered can derail a cross-functional initiative in the blink of an eye. There is no doubt that society is becoming increas- ingly digitized, and this can be a good thing—improved efficiency, enhanced quality, and better company com- pliance with ever-increasing, data-related regulatory requirements. Choosing the technology that will have the greatest positive impact on your company, in the area you most need it, is obviously a crucial decision. With production data now available for the asking, executives rightly wonder how to begin. Which data would be most beneficial? Which technologies would deliver the biggest return on investment, given a company's unique circum- stances? Which data-leakage threats are causing the most pain? This last question made headlines in 2017 with the high-profile ransomware attacks that affected Merck's operations worldwide. The industry confronted this basic question of how to begin with its first foray into big data analytics. The first step to identifying a strategy and solution is to understand what success looks like. Is the resulting anal- ysis intended to be predictive, descriptive, diagnostic, or prescriptive? The answer to that question will determine pharma's path forward and which solutions the industry should consider. T&C References 1. The Business Research Company, "The growing pharmaceuticals market: Expert forecasts and analysis," Market Research Blog, May 16, 2018, (blog.marketresearch. com/the-growing-pharmaceuticals-market-expert-fore- casts-and-analysis). 2. Ryan Cross, "Drug development success rates are higher than previously reported," Chemical and Engineering News, February 2018, page 10, (cen.acs.org/articles/96/i7/ Drug-development-success-rates-higher.html). Bikash Chatterjee is president and chief science officer at Pharmatech Associates (www.pharmatechassociates.com). The company provides product and process development, compliance, regulatory, and validation consulting and ser- vices to the life science industry. patient's optimal dose for each drug to achieve a durable response, allowing patients to live free and healthy lives during treatment. Deep learning. Deep learning, the other subset of AI, is composed of algorithms that permit software to train itself to perform tasks, such as speech and image recog- nition, by exposing multilayered neural networks to vast amounts of data. One area that is ideally suited to deep learning is the collection of natural language-derived data, such as evaluating patients against inclusion/exclu- sion criteria for clinical trials. Identifying patients who satisfy the inclusion/exclusion criteria is a key aspect of constructing a viable controlled clinical study, and for most clinical studies, any time recovered from the enroll- ment timeline can translate directly to a reduction in time-to-market. Usually, when drug developers submit details of a new trial, most of the information gets entered as structured data in formats such as drop-down menus. These data are easy to record and analyze by computers. However, patients' eligibility criteria get entered into free text fields where they can write anything they like. Traditionally, these data were nearly impossible for a computer to "understand" and interpret. Deep learning algorithms can read unstructured data so the computer can assign appro- priate clinical trials to offer the patient. Extending this concept to the treatment of patients, AI is being applied to analyze structured and unstructured clinical data, including doctors' notes and other free-text documents. Clinical data are separated into key elements while also protecting sensitive health information. The AI application then extracts thousands of these clinical data points to create a multi-dimensional profile. Doctors and researchers can then use these profiles to find suit- able candidates for a clinical trial. Blockchain Blockchain's lineage is in cryptocurrency, and the pri- mary requirement for buying and selling cryptocurrency is security, not speed or efficiency. Blockchain creates a digital ledger of all transactions that may take place in the supply chain. The application of blockchain in pharma is still in the investigative phases. One application of blockchain that is being adopted by the global supply chain is the concept of smart con- tracts. A smart contract is a computer protocol intended to digitally facilitate, verify, or enforce the negotiation or performance of a contract without third parties. In this format, contracts could be converted to computer code, stored and replicated on the system, and supervised by the network of computers that run the blockchain. This would also result in ledger feedback, such as transferring money and receiving the product or service. Interna- tional organizations, including pharma, governments, and banks are turning to blockchain to ensure and enforce the terms of their contracts.