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Target-Based Tablet Formulation

Updated 2 July 2025
  • Target-based formulation is a systematic, model-driven approach that integrates digital and self-driving processes to achieve precise tablet CQAs.
  • It employs hybrid modeling, NSGA-II, and Bayesian optimization to link raw material attributes, formulation variables, and process settings.
  • The integrated platform reliably delivers first-time-right formulations with accelerated timelines and minimal material waste.

Target-based formulation in the context of accelerated pharmaceutical tablet development refers to the systematic, model-driven process of designing tablet compositions and processes that achieve pre-specified critical quality attributes (CQAs), utilizing integrated digital and cyber-physical systems. The platform described in the cited work consists of two tightly-coupled pillars: (1) the Digital Formulator—a hybrid in-silico formulation optimizer, and (2) the Self-Driving Tableting DataFactory—an automated, robotics-driven experimental process line with real-time data analytics. The system is designed to reliably, rapidly, and resource-efficiently produce tablets meeting target quality specifications across diverse actives and excipient combinations.

1. The Digital Formulator: In-Silico, Model-Driven Target Optimization

The Digital Formulator provides the initial, data-driven solution to the target-based formulation problem. Its foundation is a hybrid, material-to-product model architecture, which links:

  • Raw material attributes (from database and supplier analytics)
  • Formulation composition variables (excipient ID, concentrations, drug loading)
  • Process settings (initial compression conditions)

to blended and tablet CQAs, specifically:

  • Flow Function Coefficient (FFC, for blend flowability)
  • Tablet porosity ε\varepsilon
  • Tablet tensile strength σ\sigma

Hybrid Modeling Approach

There are two tiers:

  • Mixture models: Estimate blend-level physical properties as functions of formulation and material inputs.
  • Process models: Deep Neural Networks (with ensemble uncertainty quantification) map blend and process parameters to final tablet CQAs.

The composite model supports generalization across APIs, validated by leave-API-out testing.

Target-Based Optimization Problem

Formulation selection is cast as multi-objective constrained optimization:

maxxFFC(x,m) s.t. E[ε]ασεεmin E[σ]βσσσmin\begin{align*} \max_{\mathbf{x}} & \quad \text{FFC}(\mathbf{x}, \mathbf{m}) \ \text{s.t. } & \quad E[\varepsilon] - \alpha \sigma_\varepsilon \geq \varepsilon_{\min} \ & \quad E[\sigma] - \beta \sigma_\sigma \geq \sigma_{\min} \end{align*}

Where:

  • x\mathbf{x}: decision variables (excipient IDs, concentrations, process setpoints)
  • m\mathbf{m}: raw material descriptors
  • E[]E[\cdot]: ensemble-predicted mean, σε,σσ\sigma_\varepsilon, \sigma_\sigma: uncertainty
  • α,β\alpha, \beta: risk factors (set to 0.2)
  • Constraints set typical targets: εmin=0.15\varepsilon_{\min}=0.15, σmin=2 MPa\sigma_{\min}=2\ \mathrm{MPa}

NSGA-II is used for optimization due to its aptitude for non-linear, multi-objective landscapes.

2. Iterative Bayesian (Self-Driving) Process Refinement

After Digital Formulator optimization, candidate formulations are advanced to the physical Self-Driving Tableting DataFactory for experimental verification and process adjustment.

Robotic Automation

Fully-integrated automation covers powder dosing, mixing, tablet compaction, real-time in-process NIR blend QC, destructive testing, and cleaning.

Bayesian Optimization Loops

Physics-Informed Bayesian Optimization (PIBO)

When optimization is limited to a single process parameter (typically main compression pressure PP), PIBO leverages empirical compaction and strength models—Kawakita and Ryshkewitch-Duckworth equations—to regularize the search:

  • Kawakita (compressibility): ε(P)=ε011+bP\varepsilon(P) = \varepsilon_0 - \frac{1}{1 + bP}
  • Ryshkewitch-Duckworth (compactability): σ(ε)=σ0ekpε\sigma(\varepsilon) = \sigma_0 e^{-k_p \varepsilon}

Bayesian Optimization, with a Gaussian Process surrogate and an acquisition function modulated by model agreement, quickly identifies pressure values yielding tablets within CQA targets—usually within six experiments.

Multi-Output Bayesian Optimization (MOBO)

Where multiple process variables (e.g., pre- and main compression, dwell time) and CQAs are considered, MOBO with a multi-output GP surrogate directly proposes new experiments, rapidly converging to the target region for all CQAs.

3. Augmented and Mixed Reality (AR/MR) in Quality Control

Real-time AR/MR overlays present critical process and CQA data directly in the tablet lab and to remote analysts:

  • Individual tablet porosity and tensile strength are visualized in green/red as each measurement occurs
  • XR dashboards allow remote, synchronous monitoring and decision-making
  • All data flows through a cloud-connected REST API for cross-lab and manufacturing floor integration

4. Validation and Transferability Across APIs and Drug Loadings

The combined workflow has been empirically validated across multiple structurally and physicochemically distinct APIs and a wide range of drug loadings.

  • The Digital Formulator delivers first-time-right formulations meeting flowability, porosity, and strength targets.
  • The Self-Driving Tableting DataFactory, through 6 or fewer iterative experiments, adapts to material and instrument variability, reducing empirical gap to target specifications (typically errors less than 0.01 in porosity, 0.1 MPa in tensile strength).
  • The entire material-to-batch cycle, from raw material entry to finished tablets, is achieved in under 24 hours, using less than 5 grams of API per case.

5. Key Algorithmic and Mathematical Details

Stage Core Algorithmic Tool Formula/Method Used Output
Digital Formulator Ensemble DNN; semi-mechanistic Hybrid mixture/product-CQA regression Candidate settings
Formulation optimization NSGA-II (multi-objective GA) max\max FFC, s.t. E[ε]εminE[\varepsilon]\geq\varepsilon_{\min}, E[σ]σminE[\sigma]\geq\sigma_{\min} Excipient/process
Process optimization PIBO; MOBO with GP surrogates GP-based acquisition, with empirical equations as constraints Setpoint refinement
Real-time QC AR/MR + REST API Direct tabletwise CQA visualization Immediate feedback

6. Significance and Implications

This integrated, target-based approach shifts the paradigm from labor- and material-intensive empirical formulation—and sequential, trial-and-error process tuning—to a closed-loop, digitally orchestrated, and AR/AI-enhanced system. The principal implications are:

  • Quantitative linkage from material attributes and process to product, enabling direct rational design
  • Robust transferability across APIs, drug loadings, and physical scales
  • Acceleration of formulation timelines to hours; minimization of API and excipient waste
  • Framework extensibility for new CQAs or manufacturing technologies

7. Limitations and Possible Extensions

While the platform demonstrates generalization across leading small-molecule APIs, ongoing extension to poorly compressible actives, alternative dosage forms, and full-scale manufacturing remains an active area of research. Incorporation of additional mechanistic knowledge or online learning may further enhance predictive performance and reduce the number of required optimization cycles.


In summary, the described system embodies a rigorous, target-based formulation paradigm enabled by digital and physical automation, advanced modeling, multi-fidelity Bayesian optimization, and immersive quality analytics, providing an effective path to on-demand, specification-driven tablet development and manufacturing.