Digital, Continuous and Data-Rich

Advances in technologies, data and development strategies are reshaping how oral solid dosage (OSD) drugs are designed and manufactured, with a clear emphasis on lowering cost, shortening timelines, and improving quality and reliability across the life cycle.¹ From ingredient sourcing and in silico modeling to continuous manufacturing and decentralized clinical trials, current thinking centers on building lean, integrated and data-rich OSD platforms that can adapt to market and regulatory pressures.²,³

Sourcing APIs and Excipients Strategically

Cost and efficiency pressures in OSD start with how active pharmaceutical ingredients (APIs) and excipients are sourced, qualified, and integrated into development plans.⁴ Organizations are increasingly linking sourcing decisions to long-term manufacturability, regulatory robustness and supply resilience — rather than just unit price.⁴ This shift includes deeper supplier qualification, contingency suppliers, and early alignment on quality attributes and regulatory documentation to reduce later-stage reformulation or filing delays.⁴

Current thinking emphasizes:

• Using standardized excipient platforms with strong regulatory precedents to streamline formulation development and simplify global submissions.⁵
• Evaluating suppliers for their process control, data transparency and change management practices to reduce variability and minimize the risk of supply disruptions or post-approval variations.⁴
• Incorporating supply chain risk assessment into target product profiles (TPPs) so that sourcing strategies are aligned with anticipated markets, dosage strengths and life cycle needs.⁵

For APIs, sourcing strategies increasingly account for particle size distribution, polymorph control and impurity profiles that affect both processing behavior and bioavailability, helping reduce trial-and-error in process development.⁵ These considerations support smoother scale-up and fewer surprises when transferring to commercial-scale OSD manufacturing.⁴

Continuous Manufacturing and Process Intensification

OSD has been a primary proving ground for continuous manufacturing (CM), which replaces the traditional stepwise batch paradigm with integrated, ‘one in, one out’ process trains.⁶ In a continuous line, raw materials flow steadily through unit operations — such as feeding, blending, granulation, drying and tableting — while finished tablets or capsules are discharged at a matching rate once steady state is reached.⁶

Economic analyses comparing CM and batch for OSD show that continuous approaches can reduce manufacturing costs and improve net present value for both brand and generic products, especially when facilities and product portfolios are designed around continuous operation.²

Benefits highlighted in the literature include:

• Lower inventory and shorter lead times, which help mitigate shortages and free working capital tied up in intermediate and finished goods.^6,7
• Reduced material waste and improved API utilization because process disturbances can be detected quickly and corrected before large volumes are affected.⁶
• Smaller equipment footprints and more flexible capacity, as the same line can be run for longer or shorter periods instead of relying on large batch vessels sized for peak demand.^2,7

From a technical perspective, CM relies heavily on process analytical technology (PAT) and advanced control strategies to monitor critical process parameters and critical quality attributes in real time.⁶ Inline spectroscopy, multipoint sensors and feedback-feedforward control loops enable dynamic adjustments to maintain product quality, supporting concepts such as real-time release testing and reducing the need for time-consuming, off-line, destructive testing.

At the same time, practical implementation requires robust process understanding, sophisticated modeling, and investment in control systems, so many organizations are pursuing hybrid approaches that combine intensified batch steps with continuous feeding, blending, or tableting where benefits are greatest.6 Regulatory authorities have signaled openness to CM, and guidance emphasizes science- and risk-based justifications for control strategies rather than prescribing specific technologies.^2,6

In Silico Tools for OSD Design

In silico technologies are increasingly used to guide OSD development decisions from preformulation onward, reducing the reliance on empirical, iterative experimentation.^3,5 These tools range from predictive models of solubility and permeability to machine learning platforms that correlate formulation and process variables with in vitro and in vivo performance.^3,5

Key applications for OSD include:

• Predicting API behavior, such as crystallinity, stability, and likely degradation pathways, to inform salt selection, solid-state form, and storage conditions.⁵
• Modeling formulation options (e.g., immediate-release vs. modified-release, enabling technologies such as spray drying or hot-melt extrusion) based on desired pharmacokinetic profiles and manufacturability constraints.^5,8
• Accelerated stability modeling and virtual shelf-life prediction, which can reduce the time and material needed for traditional long-term stability studies during early development and regulatory planning.^3,5

In silico approaches also support data-driven target product profiles by allowing teams to explore trade-offs between dosage strength, tablet size, release characteristics, and patient populations before committing to specific formulation paths.^5,9 This can reduce the risk of late-stage reformulation, bridging studies, and associated delays, which in turn helps control overall development cost.⁵

Looking ahead, integration of in silico models into digital twins of OSD processes is a key research direction, enabling virtual experimentation on changes in raw material attributes, process conditions, or equipment configurations without interrupting production.^3,6 This direction is closely tied to broader industry moves toward model-informed drug development (MIDD) and regulatory acceptance of mechanistic and data-driven models in submissions.⁹

Rethinking OSD Clinical Trials

While OSD formulation and manufacturing drive a large share of cost, clinical development strategies significantly influence both timelines and spending.¹⁰ For oral products, in silico tools, adaptive trial designs, and decentralized or hybrid clinical models are being combined to improve both efficiency and patient-centricity.^10,11

Decentralized clinical trials (DCTs) and technology-enabled hybrid models incorporate elements such as remote data capture, telemedicine visits, and local labs or pharmacies, which can reduce or replace some on-site activities.^10,11 Analyses that compare trials with and without DCT elements suggest:

• Implementation of DCT components adds upfront cost per trial phase but can lower overall trial cost through reduced screen failure rates and fewer substantive protocol amendments.¹⁰
• Phase 2 and 3 trials with DCT elements have shown reductions in screen failure rates of roughly 20-30%, translating into more efficient enrollment and lower per-patient costs.¹⁰

Digital platforms that track adherence, dosing, and patient-reported outcomes are particularly relevant for OSD studies, where long-term, at-home administration is common.¹¹ Adaptive designs, in which randomization ratios, sample sizes, or dose levels are modified based on interim analyses, further contribute by decreasing exposure to ineffective doses and focusing resources on regimens most likely to succeed.^9,12

In silico clinical trial simulations — drawing on pharmacometric models and population variability data — are increasingly used to optimize dose selection and trial design before initiation.⁹ This can lead to smaller, more focused trials that still meet statistical and regulatory standards, thereby reducing costs without compromising evidence quality.^3,9

Streamlined CMC and Regulatory Submissions

For OSD products, chemistry, manufacturing, and controls (CMC) content is a major contributor to the volume and complexity of regulatory submissions.⁵ Current thinking emphasizes building high-quality CMC packages through early cross-functional alignment and the use of predictive and digital tools rather than through late-stage document consolidation.⁵ This includes:

• Selecting excipients and process technologies with strong regulatory track records and well-characterized safety profiles, which can simplify justification and reduce regulatory questions.⁵
• Using data-rich control strategies and PAT outputs to support real-time release or reduced end-product testing, framed within a quality-by-design (QbD) narrative that links critical quality attributes to design spaces and controls.⁶

Regulatory agencies have issued guidance and reflection papers encouraging science- and risk-based approaches, including the use of modeling and simulation to justify specifications, shelf-life claims, and process flexibility.⁹ For OSD, this can enable more efficient post-approval changes, such as site or equipment changes, as long as mechanistic understanding and supportive data are well documented.^6,9

Digitalization of CMC data — through structured data standards, shared platforms, and knowledge management tools — is also seen as a path to faster, more consistent submissions and life cycle management.^3,9 By organizing data around products and processes rather than individual documents, organizations can update and repurpose information across global filings more efficiently, lowering both internal and external costs.^3,9

Testing, PAT and Quality Control

Testing and quality control strategies are central to cost and efficiency in OSD, because they influence batch release times, inventory levels, and the need for rework.^4,6 Traditional OSD manufacturing relies heavily on off-line, often destructive tests for assay, content uniformity, dissolution, and physical attributes, which can be time-consuming and resource-intensive.⁶

Advanced approaches increasingly focus on:

• In-line or at-line PAT for blend uniformity, granule moisture, and tablet attributes using spectroscopic or imaging technologies, enabling earlier detection of deviations.⁶
• Multivariate data analysis and control charts that combine process and quality data to detect trends before they lead to out-of-specification results.⁶
• Risk-based sampling plans informed by process capability and historical data, which can reduce the number of samples and tests needed for routine release while maintaining assurance of quality.⁴

In continuous lines, PAT is not just an add-on but a core element of the control strategy, enabling real-time decisions about when the process is at steady state and which material is suitable for release.⁶ This supports concepts such as diverting non-conforming material in real time, thereby limiting scrap and avoiding comprehensive rework of large volumes.⁶

In parallel, accelerated and predictive stability approaches are being leveraged to design more efficient stability programs that still meet ICH expectations.⁵ For example, algorithms that extrapolate from short-term, multi-temperature data to long-term behavior can inform shelf-life proposals and storage conditions earlier in development, guiding packaging selection and inventory planning.⁵

Future Outlook: Integrated, Data-Driven OSD Platforms

Looking ahead, the development and manufacturing of oral solid dosage products are expected to become more integrated, model-informed, and responsive, with cost and efficiency gains arising from the interaction of multiple advances rather than any single technology.^6,9 Several themes stand out in current discourse:

• Convergence of in silico modeling, CM, and PAT into closed-loop systems that can adapt in real time to variations in raw materials, environmental conditions, and demand.^3,6
• Wider adoption of DCT elements, adaptive designs, and virtual trials, coordinated with formulation and dose strategy to streamline OSD clinical development, especially in chronic and large-population indications.^9,10,11
• Expansion of digital twins and knowledge management platforms that span discovery, development, manufacturing, and post-approval changes, enabling faster, more informed decisions and more efficient regulatory interactions.^3,9

For OSD, which remains the dominant dosage form worldwide, the goal is increasingly to create end-to-end ecosystems where design, supply, production, and clinical evidence are connected through shared data and models.^4,13 Such ecosystems promise to reduce total cost of ownership, improve reliability of supply, and support more flexible and patient-centric product offerings, while maintaining the stringent quality expectations that define this segment of pharmaceutical manufacturing.^2,6

References

1. Oral Solid Dose Manufacturing: Overview, Processes, and Challenges. (2024, Sept). Adragos Pharma. [Accessed Jan 5, 2026]
2. Rossi C. V. (2022). A Comparative Investment Analysis of Batch Versus Continuous Pharmaceutical Manufacturing Technologies. Journal of pharmaceutical innovation. 17(4), 1373–1391.
3. Konagurthu, S. (2023, Sept 27). In Silico Modeling: Accelerating Drug Development. Thermo Fisher Scientific (Patheon).
4. Oral Solid Dose Manufacturing: Overview, Processes, and Challenges. (2024, Sept). Adragos Pharma. [Accessed Jan 5, 2026]
5. Five Hidden Risks of Early-phase OSD Formulation Development. (2025, June 10). Thermo Fisher Scientific (Patheon).
6. Vanhoorne, V. and Vervaet, C. (2020, April). Recent progress in continuous manufacturing of oral solid dosage forms. Int J Pharm. 579,119075.
7. Halkude, B. and Casati, F. (2023, April). EMC Advantages for Manufacturing of OSDs. CONTINUUS Pharmaceuticals.
8. Konagurthu, S. (2024, Oct). Digital Solutions to Overcome Complexities in Oral Solid Dose Drug Formulations. Contract Pharma.
9. Emili, L. and Rizk, M. In Silico Technologies: Leading the Future of Drug Development Breakthroughs. DIA Global Forum. [Accessed Jan 5, 2026]
10. DiMasi, J. et. al. (2022). Assessing the Financial Value of Decentralized Clinical Trials. Clin Ther. 44(9):e1–e15.
11. Girardin, J.L., and Seixas, A. (2024, Aug). The value of decentralized clinical trials: Inclusion, accessibility, and efficiency. Science. 386(6711):e.adq4994.
12. Increasing clinical trial success rates with adaptive technology. (2022, Sept). Huma. [Accessed Jan 5, 2026]
13. Auerbach, M. (2025, March). Advancements in Oral Solid Dosage: Innovations Shaping the Future. American Pharmaceutical Review.