High Performance Computing Options - Part 4

In the last episode we talked about the various areas that HPC and AI can impact a pharmaceutical industry. There were 10 topics I was going to talk about and we talked about the first five.

1. Drug Discovery and Development
2. Molecular Modeling and Simulation
3. Clinical Trials Optimization
4. Personalized Medicine
5. Genomic Data Analysis
6. BioInformatics
7. Drug Repurposing
8. Predictive Analytics
9. Manufacturing Process Optimization
10. Market Analysis

Let’s talk about the rest of the five today.

BioInformatics:

Ok, so Bioinformatics is a sort of a mix and match of everything. It is interdisciplinary which means it has within it Biology, Computer science, Information Engineering, Mathematics and statistics to analyze and interpret biological data.?

• Advanced Quantitative Sciences: Quantitative scientists focus on biostatistics, statistical programming, pharmacometrics, data sciences and machine learning. They provide quantitative input to drug development strategies, from early development through life-cycle management
• Bioinformatics Communities: Pharmaceutical organizations often form Bioinformatics Communities. They work on sequence and structure analysis to guide engineering efforts, selection or optimization of candidates and data capture and structure to facilitate access and sharing within this communities

Drug Repurposing:

Drug Repurposing involves finding new therapeutic uses for existing drugs. This approach significantly reduce the time and cost associated with drug development since the safety profiles of these drugs are already well established. A good example is Hydroxychloroquine. Originally developed as an anti-malarial, it is commonly used for autoimmune diseases, was explored during COVID-19 as a potential treatment and recent research also suggest it may have potential in cancer treatment by disrupting cancer cells’ ability to recycle resources.

Now any accelerated drug discovery program that looks at potential candidates will not just look at new potential candidates but also look at existing drugs and their repositioning. We talked about DEL in my previous article. DEL has the potential to identify existing drugs as repositioned candidates. MELLODDY, the project we talked about last time also has the potential because it is also in the accelerated drug discovery domain

Predictive Analytics:

Predictive Analytics is core to pharmaceutical industry and is involved everywhere. It is there in drug development and Bioinformatics to analyze biological data such as genomic sequences and metabolic pathways to identify potential targets for new therapies. It is used in clinical trial optimization enabling exploratory data analysis, extracting knowledge and insights on disease compounds and patients. It is used in manufacturing process optimization to ensure higher efficiency and quality control.

But it is not just core sciences. We must not forget that a pharma organization is not just medicines. It is also finance and accounting and legal and compliance and marketing and mergers and what not. In Finance AI can be used for Financial Planning and Forecasting where predictive analytics plays a key role. There could also potentially be a a decision engine for the next best action designed to enhance customer engagement by leveraging advanced AI / ML techniques. It could analyze HCP demographics, sales data and survey data to create a unified view of each HCPs profile. And based on that it could recommend the most effective engagement strategies tailored to each HCP.

Manufacturing Process Optimization

Data Center Edge Optimization refers to the process of enhancing the performance, efficiency and reliability of data centers located at the “edge” of the network, closer to the data source. This involves improving data processing speeds and reducing latency for manufacturing workloads and can be done using AI powered engineering solutions. It also could leverage an augmented delivery model centred on AI infrastructure. AI can also be used for data processing and latency reduction, predictive maintenance and automation and optimization.

Market Analysis

I have already covered Next Best Action as a part of Predictive Analysis, but it is a part of Marketing and engagement with the HCPs. There could be solutions that use AI and HPC to enhance its revenue management solutions, providing advanced analytics and insights to support decision making process. There is predictive analysis, automation, insights and reporting, high performance data processing, the works. And finally AI Assistant Functionality aiming at enhancing the capabilities of AI assistants to provide more accurate and actionable insights. Using Predictive Analytics of course, but Natural Language Processing to understand and respond to user queries more effectively, improving the overall experience. And HPC providing the computational power to scale up data analysis processes, allowing the AI assistant to handle complex and large-scale datasets.

So there is a tremendous amount of work going on in in the Pharmaceutical Industry where AI and HPC is already being used. Not just in the Accelerated Drug Development space but also in other areas such as Finance, Manufacturing, Marketing, etc.

Now, in Part 4 of this series, I will talk about different types of HPC models that we covered in Part 1 and talk about what can be leveraged for doing what kind of business use case. Because while it is important to understand the business, we are not all scientists and can’t get into the nitty gritty of the core sciences. But a high-level understanding is required to be able to do our jobs well. So, I will try to build that bridge of understanding needed so we can support the business the best.

But I have a question for all of you. How much science do we (Technology and Infrastructure people) need to know to support the pharma business? Do we even need to know the science? Or do we need to just know what they are going to do with our systems? Like for example, is it data intensive workloads or parallel processing workloads? Is it an interactive workload or batch processing workload? Is it memory intensive, or high throughput (large number of small independent tasks that need to be processed concurrently)?

What are your thoughts? Let me know and let’s talk.