Computational modeling for formulation design.

Formulation design is an important phase in the drug development process. However, this process at an experimental level requires exhaustive experimental work. Excipient selection, prediction of solubility, encapsulation efficiency, release patterns, drug absorption, stability, and mechanism of nanoparticle formation are some of the essential steps in formulation design. The use of various computational tools, including quantitative structure-activity relationships (QSARs), molecular modeling, molecular mechanics, discrete element modeling, finite element method, computational fluid dynamics, and physiologically based pharmacokinetics (PBPK) modeling, help in the identification of drug product inadequacies and to recommend avenues for understanding complex formulation design in less time with lower investment. Here, we focus on computational modeling tools used in formulation design and its applications.

[1]  Lawrence X. Yu,et al.  Utility of Physiologically Based Absorption Modeling in Implementing Quality by Design in Drug Development , 2011, The AAPS Journal.

[2]  Bruno C. Hancock,et al.  Process modeling in the pharmaceutical industry using the discrete element method. , 2009, Journal of pharmaceutical sciences.

[3]  R. Narayan,et al.  Localized In Situ Nanoemulgel Drug Delivery System of Quercetin for Periodontitis: Development and Computational Simulations , 2018, Molecules.

[4]  U. Nayak,et al.  Host-guest interaction study of Efavirenz with hydroxypropyl‑β‑cyclodextrin and l‑arginine by computational simulation studies: Preparation and characterization of supramolecular complexes , 2018 .

[5]  Jouko Yliruusi,et al.  3D Simulation of Internal Tablet Strength During Tableting , 2011, AAPS PharmSciTech.

[6]  A. Metwally,et al.  Computer-Assisted Drug Formulation Design: Novel Approach in Drug Delivery. , 2015, Molecular pharmaceutics.

[7]  Jasmine Gupta,et al.  Prediction of solubility parameters and miscibility of pharmaceutical compounds by molecular dynamics simulations. , 2011, The journal of physical chemistry. B.

[8]  Mohammad Hassan Khalid,et al.  Computational intelligence models to predict porosity of tablets using minimum features , 2017, Drug design, development and therapy.

[9]  A. Leach Molecular Modelling: Principles and Applications , 1996 .

[11]  Rayenne Djémil,et al.  Quantum Mechanical Study of Complexation of Dopamine and Epinephrine with β -Cyclodextrin Using PM6, ONIOM and NBO Analysis , 2012 .

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

[13]  M. Bhatia,et al.  Experimental and chemoinformatics evaluation of some physicochemical properties of excipients influencing release kinetics of the acidic drug ibuprofen. , 2015, Chemosphere.

[14]  Quantitative structure activity relationship and drug design: A Review , 2016 .

[15]  Aniket Magarkar,et al.  Rational design of liposomal drug delivery systems, a review: Combined experimental and computational studies of lipid membranes, liposomes and their PEGylation. , 2016, Biochimica et biophysica acta.

[16]  A. Bansal,et al.  Use of biorelevant dissolution and PBPK modeling to predict oral drug absorption , 2018, European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V.

[17]  C. Zhang,et al.  Quantitative Structure-Property Relationship (QSPR) Modeling of Drug-Loaded Polymeric Micelles via Genetic Function Approximation , 2015, PloS one.

[18]  M. Bhatia,et al.  Quantitative structure property relationship modeling of excipient properties for prediction of formulation characteristics. , 2016, Carbohydrate polymers.

[19]  Kamyar Haghighi,et al.  The role of multiscale computational approaches for rational design of conventional and nanoparticle oral drug delivery systems , 2007, International journal of nanomedicine.

[20]  L. Huynh Rational Design of Drug Formulations using Computational Approaches , 2013 .

[21]  J. Berg,et al.  Molecular dynamics simulations of biomolecules , 2002, Nature Structural Biology.

[22]  A. Badnjević,et al.  Applications of QSAR Study in Drug Design , 2017 .

[23]  K. Mahadik,et al.  Computational Modeling of Polymeric Physicochemical Properties for Formulation Development of a Drug Containing Basic Functionality. , 2017, Journal of pharmaceutical sciences.

[24]  Aibing Yu,et al.  Coordination Number of the Packing of Ternary Mixtures of Spheres: DEM Simulations versus Measurements , 2011 .

[25]  Kyle V. Camarda,et al.  A molecular design approach to peptide drug stabilization , 2006 .

[26]  Bodhisattwa Chaudhuri,et al.  Understanding granular mixing to enhance coating performance in a pan coater: Experiments and simulations , 2011 .

[27]  Göran Frenning,et al.  Compression mechanics of granule beds : A combined finite/discrete element study , 2010 .

[28]  Amiram Goldblum,et al.  Computer-aided design of liposomal drugs: In silico prediction and experimental validation of drug candidates for liposomal remote loading. , 2014, Journal of controlled release : official journal of the Controlled Release Society.

[29]  A. Baroutaji,et al.  Mechanics and Computational Modeling of Pharmaceutical Tabletting Process , 2014 .

[30]  Wei Xu,et al.  Advances and challenges in PBPK modeling--Analysis of factors contributing to the oral absorption of atazanavir, a poorly soluble weak base. , 2015, European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V.

[31]  Vaibhav Jain,et al.  Pharmacoinformatic approaches to understand complexation of dendrimeric nanoparticles with drugs. , 2014, Nanoscale.

[32]  M. Karplus,et al.  Dynamics of folded proteins , 1977, Nature.

[33]  Peter L D Wildfong,et al.  The Application of Modeling and Prediction to the Formation and Stability of Amorphous Solid Dispersions. , 2018, Journal of pharmaceutical sciences.

[34]  José Mario Martínez,et al.  PACKMOL: A package for building initial configurations for molecular dynamics simulations , 2009, J. Comput. Chem..

[35]  Anil Jindal,et al.  MOLECULAR MODELLING: A NEW SCAFFOLD FOR DRUG DESIGN , 2013 .

[36]  Neil Parrott,et al.  Applications of physiologically based absorption models in drug discovery and development. , 2008, Molecular pharmaceutics.

[37]  M. Shaikh,et al.  Self nanoemulsifying granules (SNEGs) of meloxicam: preparation, characterization, molecular modeling and evaluation of in vivo anti-inflammatory activity , 2017, Drug development and industrial pharmacy.

[38]  Y. Z. Chen,et al.  Formulation development of transdermal dosage forms: quantitative structure-activity relationship model for predicting activities of terpenes that enhance drug penetration through human skin. , 2007, Journal of controlled release : official journal of the Controlled Release Society.

[39]  Josep Ramón Goñi,et al.  Molecular dynamics simulations: advances and applications , 2015, Advances and applications in bioinformatics and chemistry : AABC.

[40]  Pietro Sartorelli,et al.  Prediction of percutaneous absorption from physicochemical data: A model based on data of in vitro experiments , 1998 .

[41]  M. Maniruzzaman,et al.  Molecular modeling as a predictive tool for the development of solid dispersions. , 2015, Molecular pharmaceutics.

[42]  Bruno C. Hancock,et al.  Modelling the mechanical behaviour of pharmaceutical powders during compaction , 2005 .

[43]  D. A. Teixeira,et al.  Computational and experimental approaches for development of methotrexate nanosuspensions by bottom-up nanoprecipitation. , 2017, International journal of pharmaceutics.

[44]  Fernando J. Muzzio,et al.  A modeling approach for understanding effects of powder flow properties on tablet weight variability , 2009 .

[45]  Neha Goyal,et al.  Surging footprints of mathematical modeling for prediction of transdermal permeability , 2017, Asian journal of pharmaceutical sciences.

[46]  Jae-Hyuk Yu,et al.  Solubility and bioavailability enhancement of ciprofloxacin by induced oval-shaped mono-6-deoxy-6-aminoethylamino-β-cyclodextrin. , 2017, Carbohydrate polymers.

[47]  Colin Thornton,et al.  A coupled DEM/CFD analysis of the effect of air on powder flow during die filling , 2009 .

[48]  John Crison,et al.  Predicting feasibility and characterizing performance of extended-release formulations using physiologically based pharmacokinetic modeling. , 2012, Therapeutic delivery.

[49]  Chun-Jen Yang,et al.  Construction of a quantitative structure-permeability relationship (QSPR) for the transdermal delivery of NSAIDs. , 2009, Journal of controlled release : official journal of the Controlled Release Society.

[50]  Rama Rao Nadendla,et al.  Molecular modeling: A powerful tool for drug design and molecular docking , 2004 .

[51]  U. Nayak,et al.  Inclusion Complexation of Etodolac with Hydroxypropyl-beta-cyclodextrin and Auxiliary Agents: Formulation Characterization and Molecular Modeling Studies. , 2017, Molecular pharmaceutics.

[52]  J. Crison,et al.  Mathematical Model-Based Accelerated Development of Extended-release Metformin Hydrochloride Tablet Formulation , 2015, AAPS PharmSciTech.