Table III.

Summarization of Input-Output Data Used to Build Various ANN Models in Different Pharmaceutical Formulation Studies

Dataset size	Inputs/variables	Output(s)	Purpose	Reference
125	19 variables related to: - the composition of the formulations - the processing conditions	- Time taken for 10% of the drug to be released - Time taken for 90% of the drug to be released	Prediction of the most important formulation and processing variables contributing to the in vitro dissolution of sustained-release (SR) minitablets	(70)
Two datasets: 154 (for synthetic samples) 169 (for pharmaceutical samples)	- 5 principle components for synthetic samples - 6 principle components for pharmaceutical samples	Concentrations of 3 vitamins in synthetic and pharmaceutical samples	Prediction of vitamins in synthetic and pharmaceutical samples	(71)
30	3 input variables: - acid concentration - acid solution to chitin ratio - reaction time	Percentage production yield of glucosamine	Prediction of glucosamine production yield from chitin under various reaction conditions	(72)
180	4 input variables related to different formula ingredients: - Methocel® K100M - xanthan gum - Carbopol® 974P - Surelease®	In vitro dissolution time profiles at six different sampling times	Development and optimization of sustained-release salbutamol sulfate formulation	(73)
300	5 input variables related to 5 active ingredients and excipients (three physical–chemical properties of active ingredients in addition to two formulation factors): - solubility - mean particle size - specific surface area - the weight ratios of microcrystalline cellulose - the weight ratios of magnesium stearate	Tablet tensile strength and disintegration time before and after accelerated test	Prediction of responses to differences in quantities of excipients and physical–chemical properties of active ingredients in tablets	(74)
327	6 input variables related to 14 active ingredients: - melting point - solubility - specific surface area - mean particle size - size distribution - contents of APIs	- Tablet tensile strength - Disintegration time	Prediction of the contribution of different physicochemical properties of APIs to tablet properties	(75)
15	3 formulation factors: - weight ratio of drug to lipid - the concentration of polymer - the concentration of surfactant	- Drug loading efficiency - Mean particle size	Optimization of controlled-release nanoparticle formulation	(76)
45	3 input variables: - chitosan (Cs) concentration - potasodium tripolyphosphate (TPP) concentration - mass ratio of Cs and TPP	- Nanoparticle size - Percentage yield	Optimization of formulation parameters of chitosan-tripolyphosphate nanoparticles	(77)
43	7 input variables: - alginate percentage - concentration of CaCl2 solution in the emulsion - percentage of Tween™ 85 in the emulsion - percentage of Tween™ 85 in the receptor bath - flow rates of alginate - flow rates of emulsion - frequency of vibration	- Shape - Oil content - Oil distribution	Optimization of encapsulation of active pharmaceutical ingredients (API) for efficient delivery of hydrophobic compounds	(78)
20	3 input variables: - the amounts of drug (pilocarpine hydrochloride) - the amounts of bile salt (sodium deoxycholate) - the amounts of water	Entrapment efficiency	Optimization of ocular formulation of flexible nano-liposomes containing pilocarpine hydrochloride	(79)
16	3 input variables: - amount of oil - amount of surfactant - amount of co-surfactant	Minimal globule size	Optimization of self-emulsifying drug delivery system	(80)
8	2 formulation variables: - ratio of carrier to coating - type of solubilizing agent	Amount of API resealed in 10 min and 30 min	Development of a new liquisolid formulation	(81)
160	160 NIR and Raman spectral data of each of intact tablets	Dissolution of the tablets	Prediction of the in vitro dissolution of pharmaceutical tablets	(82)
29	4 formulation and process variables: - microcrystalline cellulose concentration - sodium starch glycolate concentration - spheronization time - extrusion speed	- Drug release (at 15 min, 30 min, 45 min, and 60 min) - Aspect ratio - Yield	Prediction of the effects of formulation and process variables on drug release	(83)
144	Amino acid composition of each monoclonal antibody and different formulation conditions (i.e., pH and salt concentrations)	- Melting temperature - Aggregation onset - Temperature - Interaction parameter	Prediction of biophysical properties of therapeutic monoclonal antibodies	(84)
32	4 input variables: - concentration of shell material - concentration of core material - type of shell material - type of core material	- Tensile strength - Brittleness index	Prediction of powder compact ability of tablets using core/shell technique	(85)
646	24 variables related to: - formulation (including molecular weight, melting point, hydrogen bonding for both drug and polymer) - experimental conditions (including temperature, relative humidity, and storage time)	Stability results	Prediction of the physical stability of solid dispersions	(86)