DECEMBER - 20229TECH OUTLOOKwere nearly 7,000 anticancer drugs in the R&D pipeline, representing a 7 percent increase over 2020 and outpacing the overall rate of pipeline growth. Almost all of this pipeline is geared toward precision oncology, including therapies such as small molecule angiogenesis inhibitors, immune checkpoint modulators, T cell­engaging antibodies, antibody­drug conjugates, and chimeric antigen receptor (CAR-T) therapies, among others.By design, precision oncology requires a drug development framework that can bend to the specific needs of cancer patients and the unique genetic and molecular characteristics of their tumors. The inherent heterogeneity means the path from laboratory to finished product and commercial launch will be different for every therapy. Building an optimal road map for each requires a deep understanding of those differences and careful integration of best practices to meet patients' needs and more quickly bring novel therapies to market.From a manufacturing perspective, the implications are significant. Matching drug products to clinical and commercial needs for such a robust pipeline is inherently challenging and doing so in the shadow of a global pandemic increases the complexity by an order of magnitude. Targeted cancer therapies are more complex than conventional chemotherapies, making their formulation more challenging, and many are highly potent compounds that require specialized facilities, equipment, and handling. Similarly, biologics must be handled, stored, and shipped at low temperatures to ensure the physical integrity of the doses and cell-based immunotherapies have unique logistical obstacles. Coupled with quickly evolving standards of care across cancer types, accelerated approval pathways, lower production volumes, shorter product lifecycles, and a crowded development field, these considerations add multiple layers of intricacy to an already complex development model.Starting at the Finish Line with Predictive ModelingDesigning formulations for targeted oncology therapies--both small molecules and biologics--presents several challenges related to the complexity of the substances, the need for noveland sophisticated delivery routes and production methods, new regulatory pathways, and accelerated timelines for getting these products to market. To reduce the risk that under performing formulations will slow development programs at any point along the development continuum, formulations must be designed from the outset to align with the final container and drug-delivery method to avoid discordancy between active ingredients and production materials. In addition, sponsors and strategic partners should maintain a consistent line of sight to the end goal-- development of a safe, effective medication that meets the needs of the patients who will be receiving it.Understanding patient and commercialization needs is critical for defining strategies for stabilization, concentration, and delivery. In addition to the well-established principles of formulation noted above, the use of creative and often proprietary formulation technologies enables development teams to overcome unique formulation challenges. The Quadrant 2computational modeling platform is an example. It is an integrated drug formulation program that encompasses in silico tools, high-throughput screening, and predictive tools that can integrate with the commercialization process. The program analyzes the specific molecular structure and chemical characteristics of compounds in combination with the unique target product profile to predict the optimal solubility enhancement technology and excipient combination at the earliest stage of development. Further modeling can predict stability outcomes for shelf life and component compatibility, blending and compression performance, and even product pharmacokinetic behavior to accelerate formulation and process development.Capabilities and technologies such as these are becoming crucial differentiators. In addition to streamlining development time and mitigating the inherent risks associated with trial-and-error experimental approaches, predictive modeling can help identify specific patient populations who might not tolerate certain drugs well or those at greater risk of an adverse drug reaction. They can also help companies better understand how demand will be affected by variables such as regional demographics or economic conditions. This kind of insight ensures the most efficient use of resources, but more importantly, it improves patient access to critical medicines.The Road to SuccessAs researchers continue to develop new methods of targeting cancer cells and more agents reach the market, competition for key targets will become more intense. To be successful, pharma companies active in oncology will need to adopt new development strategies for getting the right drug to the right patient at the right time. Increasingly, this requires shifting to a more patient-centric approach and developing strategic outsourcing relationships to address the unique formulation, manufacturing, regulatory, and supply chain challenges associated with precision oncology therapies. Building an optimal road map for each requires a deep understanding of those differences and careful integration of best practices to meet patients' needs and more quickly bring novel therapies to market
< Page 8 | Page 10 >