Bayesian probabilistic modeling in pharmaceutical process development

[1]  B. Muthén,et al.  A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the metropolis-hastings algorithm , 1998 .

[2]  Carlo Castagnoli,et al.  Application of Quality by Design Principles for the Definition of a Robust Crystallization Process for Casopitant Mesylate , 2010 .

[3]  Chris Vervaet,et al.  Reduction of tablet weight variability by optimizing paddle speed in the forced feeder of a high-speed rotary tablet press , 2015, Drug development and industrial pharmacy.

[4]  Olaf Wolkenhauer,et al.  How Modeling Standards, Software, and Initiatives Support Reproducibility in Systems Biology and Systems Medicine , 2016, IEEE Transactions on Biomedical Engineering.

[5]  San Kiang,et al.  Can pharmaceutical process development become high tech , 2006 .

[6]  William F. Kiesman,et al.  Case Studies in the Development of Drug Substance Control Strategies , 2015 .

[7]  Jacob Albrecht,et al.  PROBABILISTIC MODELS FOR FORECASTING PROCESS ROBUSTNESS , 2019 .

[8]  Lawrence X. Yu,et al.  The future of pharmaceutical quality and the path to get there. , 2017, International journal of pharmaceutics.

[9]  Michael Mitchell,et al.  Risk Management in the Pharmaceutical Product Development Process , 2008, Journal of Pharmaceutical Innovation.

[10]  John J. Peterson,et al.  A Bayesian Reliability Approach to Multiple Response Optimization with Seemingly Unrelated Regression Models , 2009 .

[11]  G. Reklaitis,et al.  Perspectives on the continuous manufacturing of powder‐based pharmaceutical processes , 2016 .

[12]  Ignacio E. Grossmann,et al.  An index for operational flexibility in chemical process design. Part I: Formulation and theory , 1985 .

[13]  J. Rantanen,et al.  The Future of Pharmaceutical Manufacturing Sciences , 2015, Journal of pharmaceutical sciences.

[14]  Connor W. Coley,et al.  Machine Learning in Computer-Aided Synthesis Planning. , 2018, Accounts of chemical research.

[15]  John Salvatier,et al.  Probabilistic programming in Python using PyMC3 , 2016, PeerJ Comput. Sci..

[16]  Koichi Tanaka,et al.  Highly selective photoreactions of α-oxoamides and α-tropolone alkyl ethers in crystalline inclusion complexes , 1987 .

[17]  Narayan Variankaval,et al.  From Form to Function: Crystallization of Active Pharmaceutical Ingredients , 2008 .

[18]  T. De Beer,et al.  Optimization of a pharmaceutical freeze-dried product and its process using an experimental design approach and innovative process analyzers. , 2011, Talanta.

[19]  G. K. Raju,et al.  Understanding Pharmaceutical Quality by Design , 2014, The AAPS Journal.

[20]  Ravikanth Kona,et al.  Quality by Design I: Application of Failure Mode Effect Analysis (FMEA) and Plackett–Burman Design of Experiments in the Identification of “Main Factors” in the Formulation and Process Design Space for Roller-Compacted Ciprofloxacin Hydrochloride Immediate-Release Tablets , 2012, AAPS PharmSciTech.

[21]  D. Knill,et al.  The Bayesian brain: the role of uncertainty in neural coding and computation , 2004, Trends in Neurosciences.

[22]  Paul C. Collins,et al.  Chemical engineering and the culmination of quality by design in pharmaceuticals , 2018 .

[23]  John J. Peterson,et al.  A Bayesian Approach to the ICH Q8 Definition of Design Space , 2008, Journal of biopharmaceutical statistics.

[24]  Klavs F Jensen,et al.  Reconfigurable system for automated optimization of diverse chemical reactions , 2018, Science.

[25]  V. Venkatasubramanian.  The promise of artificial intelligence in chemical engineering: Is it here, finally? , 2018, AIChE Journal.

[26]  Jun Li,et al.  Current complexity: a tool for assessing the complexity of organic molecules. , 2015, Organic & biomolecular chemistry.

[27]  Lawrence X. Yu.  Pharmaceutical Quality by Design: Product and Process Development, Understanding, and Control , 2008, Pharmaceutical Research.

[28]  Jun Li,et al.  A data-driven strategy for predicting greenness scores, rationally comparing synthetic routes and benchmarking PMI outcomes for the synthesis of molecules in the pharmaceutical industry , 2017 .

[29]  Salvador García-Muñoz,et al.  Definition of Design Spaces Using Mechanistic Models and Geometric Projections of Probability Maps , 2015 .

[30]  E. Wagenmakers,et al.  Bayesian benefits with JASP , 2017 .

[31]  Ignacio E. Grossmann,et al.  Optimal process design under uncertainty , 1983 .

[32]  Venkat Venkatasubramanian,et al.  Leveraging Bayesian Approach to Predict Drug Manufacturing Performance , 2016, Journal of Pharmaceutical Innovation.

[33]  James M. Carothers,et al.  Data science: Accelerating innovation and discovery in chemical engineering , 2016 .

[34]  Jun Li,et al.  Evolving Green Chemistry Metrics into Predictive Tools for Decision Making and Benchmarking Analytics , 2018 .

[35]  John J. Peterson,et al.  Predictive Distributions for Constructing the ICH Q8 Design Space , 2017 .

[36]  Daniel S. Silver.  The New Language of Mathematics , 2017 .

[37]  Thomas A. Little,et al.  Process and Method Variability Modeling to Achieve QbD Targets , 2016, AAPS PharmSciTech.

[38]  Alan D. Braem,et al.  APPLICATIONS OF QUALITY RISK ASSESSMENT IN QUALITY BY DESIGN (QbD) DRUG SUBSTANCE PROCESS DEVELOPMENT , 2019, Chemical Engineering in the Pharmaceutical Industry.

[39]  Benjamin Debrus,et al.  A Bayesian Design Space for Analytical Methods Based on Multivariate Models and Predictions , 2013, Journal of biopharmaceutical statistics.

[40]  John J. Peterson,et al.  The ICH Q8 Definition of Design Space: A Comparison of the Overlapping Means and the Bayesian Predictive Approaches , 2010 .

[41]  Johannes G Khinast,et al.  Use of mechanistic simulations as a quantitative risk-ranking tool within the quality by design framework. , 2014, International journal of pharmaceutics.

[42]  Victor E. Kane,et al.  Process Capability Indices , 1986 .

[43]  John J. Peterson,et al.  A Bayesian Approach for Multiple Response Surface Optimization in the Presence of Noise Variables , 2004 .

[44]  John J. Peterson.  A Posterior Predictive Approach to Multiple Response Surface Optimization , 2004 .

[45]  Klavs F. Jensen,et al.  Flow chemistry—Microreaction technology comes of age , 2017 .

[46]  A. Gelman,et al.  Stan , 2015 .

[47]  Carl D. Laird,et al.  An Optimization-Based Framework to Define the Probabilistic Design Space of Pharmaceutical Processes with Model Uncertainty , 2019, Processes.

[48]  John J. Peterson,et al.  Batch-to-Batch Variation: A Key Component for Modeling Chemical Manufacturing Processes , 2015 .

[49]  John J. Peterson,et al.  A Bayesian Design Space Approach to Robustness and System Suitability for Pharmaceutical Assays and Other Processes , 2009 .

[50]  Veronika Debevec,et al.  Scientific, statistical, practical, and regulatory considerations in design space development , 2018, Drug development and industrial pharmacy.

[51]  Andrew Gelman,et al.  The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo , 2011, J. Mach. Learn. Res..

[52]  Enrique Del Castillo,et al.  Model and Distribution-Robust Process Optimization with Noise Factors , 2005 .

[53]  Jacob Albrecht,et al.  Estimating reaction model parameter uncertainty with Markov Chain Monte Carlo , 2013, Comput. Chem. Eng..

[54]  Lawrence X. Yu,et al.  Emerging technology: A key enabler for modernizing pharmaceutical manufacturing and advancing product quality. , 2016, International journal of pharmaceutics.

[55]  Andrew Thomas,et al.  WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..