Streamlining ISS Strategy for Enhanced Data Generation in a Specialty Pharmaceutical Firm

The Challenge

As a speciality pharmaceutical company with a relatively new Medical Science Liaison (MSL) force, our client recognised the need to optimise their data generation capabilities. They sought the expertise of MMD to work on their Investigator-Sponsored Studies (ISS) strategy. This support aimed to ensure a steady scientific and promotional communications data stream. This collaborative effort addressed our concerns about our client’s ISS’s frequency, cost, and duration.

Developing a Comprehensive Strategy

MMD’s approach was comprehensive. We began by extensively collaborating with the company’s scientific (MSLs, Medical Affairs) and commercial (Marketing, Sales, Market Access) teams. We meticulously mapped out the scientific and promotional data requirements for both the short and long term. In addition, we conducted a detailed analysis of their major competitor data and key messages. This rigorous process led to a data generation strategy that fully aligned with their needs and instilled confidence in the strategy.

“Thank you so much; you have solved our data generation issues. I am sure, we will now have a regular stream of externally usable data.”​ Senior Medical Affairs Leader

Streamlining the ISS Process

Moreover, we worked on optimising the ISS process and reviewing and refining approval processes. We wrote Standard Operating Procedures (SOPs), developed templates, and provided training to all involved to ensure consistency. Next, we developed external materials and collaborated with their agency to create a website for ISS.​

Results

As a result, the speciality pharmaceutical company was able to benefit from the following:

• Our comprehensive data generation strategy, tailored for both short—and long-term needs, filled the gaps and provided a clear roadmap for future data collection. This strategy fully aligned with the company’s data generation needs, ensuring a steady stream of scientific and promotional communications data.
• To support the new strategy, we introduced a robust system, streamlined processes, and user-friendly templates. These tools were designed to simplify and standardise the ISS process, making it more efficient and cost-effective.
• Our efforts in aligning and training the teams have equipped them with the necessary skills and knowledge to implement the new strategy effectively. They are now well-prepared to drive the ISS process forward, ensuring a steady data stream for the company’s scientific and promotional communications. By working closely with the company and embracing a systematic approach, MMD optimised the investigator-sponsored studies process, enabling the speciality pharmaceutical company to enhance its data generation capabilities.

MMD provides tailored support to businesses by optimising data generation capabilities and enhancing investigator-sponsored studies (ISS) strategies. By collaborating closely with clients, MMD aligns scientific and promotional data requirements with short—and long-term goals, ensuring a competitive edge in the market. MMD offers comprehensive solutions to meet each business’s unique needs through process optimisation, system implementation, and team alignment.

To explore MMD’s services and discuss how they can support your organisation’s success, visit their website or contact their team via email or phone. Begin the conversation and unlock your business’s potential.

Transforming Pharmaceutical Research with AI-driven Drug Discovery and ML-enhanced Development.

The pharmaceutical industry continuously strives to innovate and accelerate drug discovery and development. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful tools to drive this process forward with increased efficiency and accuracy. This article discusses how AI and ML revolutionise drug discovery and development, benefiting the industry and patients worldwide.

AI and ML in Drug Discovery

Drug Target Identification and Validation

AI and ML can analyse large volumes of biological and chemical data in a fraction of the time it would take through traditional methods. This enables researchers to identify and validate drug targets more effectively. In addition, by mining high-throughput screening and omics data, AI can predict previously unrecognised biological interactions and potential drug candidates, providing new avenues for therapy development.

In Silico Compound Screening and Design

The use of AI algorithms and ML models in the in-silico screening of large compound libraries expedites the identification of potential drug candidates. This use considerably reduces the time and resources required. Furthermore, AI-driven molecular design can generate novel compounds with desired properties, such as increased stability, bioavailability, and specificity, while minimising unintended side effects.

Predicting Drug-Target Interactions

ML models, such as deep learning algorithms, can predict drug-target interactions more accurately than conventional methods. Identifying these interactions early in the drug discovery process can help researchers focus on the most promising candidates, avoiding costly and time-consuming dead-ends or delays.

AI and ML in Drug Development

Clinical Trial Design and Optimisation

AI-powered systems can analyse vast amounts of clinical trial data, such as patient demographics, enrolment rates, and treatment outcomes, to optimise trial design and execution. By doing so, they can inform researchers of potential clinical trial issues in advance, leading to more streamlined and cost-effective trials while reducing the risk of failure or delays.

Patient Selection and Recruitment

The most effective clinical trials require identifying and enrolling the right patient population. AI and ML can predict patient response based on biomarker data and other factors, such as patient demographics and medical history. This support enables the identification of the most suitable patient populations for a specific drug candidate, reducing the time and cost associated with trial recruitment while increasing the likelihood of successful outcomes.

Biomarker Discovery and Validation

Biomarkers are remarkably valuable in pharmaceutical research, as they can indicate disease progression, drug response, and potential side effects or adverse events. AI and ML are transforming how biomarker identification and validation by analysing vast amounts of multi-omics and clinical data, allowing researchers to draw meaningful insights that can inform drug discovery, development, and personalised medicine initiatives.

Post-market Surveillance

Following the approval and launch of a pharmaceutical product, ongoing monitoring for safety and effectiveness is essential to detecting and addressing any potential issues or adverse events. AI-driven pharmacovigilance systems can rapidly process large volumes of data, identifying safety signals and trends and providing valuable real-time information to developers and regulatory authorities.

For instance, the application of AI and ML in drug discovery and development has led to the identification of novel drug targets that were previously overlooked, the design of more effective and safer drug candidates, and the optimisation of clinical trials, leading to faster and more cost-effective drug development. These cutting-edge technologies offer renewed hope for discovering and developing novel, life-saving therapies more efficiently and practically, ultimately benefitting the industry and the patients who rely on its innovations.

In conclusion, Artificial Intelligence and Machine Learning have immense potential to transform drug discovery and development. To fully harness this potential, expert medical writing and communication play a crucial role in deciphering complex data throughout the product lifecycle. By integrating robust communication plans across your business and effectively conveying key messages, you can ensure that AI-driven innovations are well-understood, leading to more efficient workflows, improved decision-making, and exceptional outcomes across the healthcare industry. Embrace the future through seamless collaboration with AI in medical writing and communication.