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Related Experiment Videos

Machine Learning-Driven Techno-Economic Uncertainty Analysis in Batch Pharmaceutical Manufacturing.

Diego Andres Rueda Ordonez1, Letícia Costa da Silva Mesquita1, Amanda Lemette Teixeira Brandão1

  • 1Department of Chemical and Materials Engineering, Pontifical Catholic University of Rio de Janeiro, 225 Marquês de São Vicente Street, Gávea, Rio de Janeiro, RJ 22451900, Brasil.

ACS Omega
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning (ML) framework to estimate techno-economic uncertainties in active pharmaceutical ingredient (API) production. The ML approach significantly reduces computational burden, enabling faster and more effective cost and profitability assessments for batch manufacturing.

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Area of Science:

  • Chemical Engineering
  • Pharmaceutical Manufacturing
  • Computational Science

Background:

  • Active pharmaceutical ingredients (APIs) are manufactured in batch multiproduct facilities, necessitating efficient scale-up for cost reduction and productivity enhancement.
  • Traditional uncertainty analyses in API production are computationally intensive and time-consuming due to reliance on extensive simulations.
  • Assessing techno-economic uncertainties is critical for optimizing production costs and profitability in pharmaceutical manufacturing.

Purpose of the Study:

  • To develop and validate a machine learning (ML)-based framework for estimating techno-economic uncertainties in batch API production.
  • To streamline the assessment of cost and profitability by reducing the computational burden of traditional sensitivity analyses.
  • To provide a fast and effective tool for estimating unit production cost (UPC) and minimum product selling price (MPSP) distributions in pharmaceutical manufacturing.

Main Methods:

  • A pharmaceutical production simulation model was employed for the synthesis of an API.
  • A machine learning (ML) framework, utilizing various algorithms (tree-based, instance-based, linear, polynomial, kernel-based), was developed to predict techno-economic parameters.
  • Techno-economic uncertainty analysis was performed to determine the impact on key financial metrics like UPC, MPSP, and internal rate of return (IRR).

Main Results:

  • A baseline unit production cost (UPC) of USD 175/kg was estimated for an annual API output of 33,000 kg.
  • ML predictions indicated UPC variations between USD 140/kg and USD 240/kg, influencing the minimum product selling price (MPSP).
  • The minimum product selling price (MPSP) to achieve a 30% internal rate of return (IRR) ranged from USD 262/kg to USD 525/kg, with a 90% confidence interval for MPSP fluctuating between USD 200/kg and USD 900/kg.

Conclusions:

  • The proposed ML framework significantly reduces computational demands compared to traditional sensitivity analyses.
  • This ML-driven approach offers a rapid and efficient method for estimating unit production cost (UPC) and minimum product selling price (MPSP) distributions.
  • The ML-based techno-economic assessment (TEA) is a valuable, standalone tool for cost assessment in batch production across pharmaceutical, chemical, and biochemical industries.