As pharmaceutical and biotech companies look at priorities for the remainder of the year, and into 2025, addressing formulation challenges should be at the top of the list. When developing new oral solid dose (OSD) drug candidates, pharma companies often have to combat low solubility and bioavailability challenges, which need to be resolved before drug candidates can move to clinical trials. Innovative digital technologies, such as predictive modeling, artificial intelligence (AI) and machine learning (ML), offer overall efficiencies and informed decision-making in early development, reducing the time and costs associated with bringing new drugs to market. Solubility of OSD formulations plays a crucial role in the creation of accessible and easily administered medications so that patients around the world have access to efficacious therapies. Poorly soluble compounds often demonstrate lower bioavailability, which can reduce drug efficacy. This is a prevalent concern affecting a significant percentage of active pharmaceutical ingredients (APIs). It is common for pharmaceutical and biotech companies to encounter these complexities throughout early development, and they must lean on innovative solutions to overcome these challenges. Utilizing a diagnostic tool in the early stages, for example, can help by enabling in-silico predictions of formulations. Astoundingly, studies have shown that approximately 90 percent of drugs then fail in clinical trials, primarily due to efficacy or safety concerns. Fail rates could be mitigated by better-formulated drugs with the right chemistries for solubility and bioavailability. Historically, pharmaceutical companies have used a trial-and-error approach to find effective formulations, which was inefficient, costly and often resulted in delays in delivering vital medications to patients. In contrast to the many months typically spent on trial-and-error, pharma players can now leverage a platform that saves time and money by quickly providing a solution to enhance solubility. At all stages of the journey, innovation is an integral part of getting therapies to patients faster. Overcoming complexities in early drug development  The solubility and bioavailability of a molecule depend on various physio-chemical factors, as well as the properties of the excipients in the formulation. To determine the most effective combinations for a drug, scientists must run detailed experiments, collect a variety of data and conduct a thorough analysis. This process can be time consuming and costly. By leveraging tools like AI and ML, pharma researchers can remove the guesswork and accurately predict the most effective formulations in early drug development, saving time during a critical step in the process. Many pharma and biotech companies find it beneficial to collaborate with a Contract Development and Manufacturing Organization (CDMO) specializing in early drug development. A CDMO provides comprehensive services and access to expertise and innovative technologies, all of which help address poor solubility and drive overall efficiencies in the drug development process. A CDMO who understands how to navigate the intricacies of formulation can help pharma companies achieve their overall goal of getting life-saving therapies to patients as quickly as possible. The benefits of leveraging digital innovation Advancements in AI and ML technologies are revolutionizing formulation development, as they offer predictive modeling solutions that address the long-standing challenges of solubility and bioavailability early in the process, and are showing promising progress via an initial wave of AI-developed drugs in the pipeline. Platforms leverage in-silico predictive modeling for aiding in early formulation development. These can quickly analyze a compound’s specific molecular structure and chemical characteristics, in combination with a unique drug target profile, and predict the optimal solubility enhancement technology and excipient combinations that are most likely to succeed. These insights also help mitigate the risk of poor solubility after proof of concept, which can be a costly setback in the drug development process. AI-generated insights provide highly accurate results and are more reliable than traditional trial-and-error methods. However, AI algorithms must incorporate a variety of reliable sources of data to ensure the accuracy of the resulting models. These innovative digital solutions are being integrated across the pharmaceutical industry – from early drug development to clinical trials and manufacturing – as they offer benefits of efficiency, efficacy and safety, as well as patient-centricity and speed to market. A technological revolution in drug formulation  As the industry further embraces digitalization and next-generation technologies, the impact on drug development will be profound. Pharmaceutical companies can accelerate the journey from molecule to medicine, potentially improving outcomes and accessibility, so patients can live longer, fuller lives. According to McKinsey & Company, AI is expected to produce upwards of $110 billion in annual value across the pharmaceutical supply chain. Additionally, the surge in interest and advancements in AI and ML technologies, coupled with an increase in regulatory submissions involving these methods (exceeding 100 in 2021), has led the U.S. Food and Drug Administration (FDA) to establish multiple specialized groups. These groups are tasked with accumulating knowledge, providing support, assessing and even driving innovation in AI and ML applications within the pharmaceutical sector. This trend is further underscored by the anticipated growth of the global AI market in drug discovery, which is projected to reach a value of $4.9 billion by 2028. As the industry evolves, technological advancements will be crucial for addressing long-standing challenges and unlocking new opportunities in developing more effective, accessible treatments. With the integration of AI and ML, the future of drug development holds vast potential, benefiting both the pharmaceutical industry and patients globally.