Liquisolid Technique For Solubility Enhancement of a Poorly Soluble Thrombin Inhibitor: Optimization Using Design of Experiments and Artificial Neural Networks

Abstract

The current work uses liquisolid (LS) technology to increase the absorption efficiency and dissolution rate of the weakly water-soluble drug dabigatran etexilate (DE) by formulating it using the design of experiments (DoE) and comparing it with the artificial neural network (ANN). D-optimal design in Stat-Ease software was used to formulate and optimize DE liquisolid capsules. The drug-to-nonvolatile solvent ratio (X1 – 25 to 50), carrier-to-coating ratio (X2 – 10 to 30), type of nonvolatile solvent (X3 – PEG 400 or Kolliphor EL) and type of carrier (X4 – Neusilin or Fujicalin) were taken as independent variables and percentage drug release (Y1) and angle of repose (Y2) were taken as dependent variables. The results were compared to artificial neural networks (ANN) utilizing JMP software to improve the prediction of the chosen output variables. The optimized formulation was developed and evaluated. The R2 value of the D-optimal design for percentage drug release was 0.914, whereas for ANN, it was 0.943. The mean square error (MSE) value of the quadratic model obtained in the D-optimal design was 90.76, and in ANN, it was only 2.392. The R2 value for the quadratic model in the D-optimal design for the angle of repose was 0.723, whereas for ANN, it was 0.751. The MSE for the D-optimal design was 28.11, whereas for ANN, it was 11.04. Based on the analysis of results, PEG400 was selected as the nonvolatile solvent and Neusilin as a carrier for optimized formulation. The percentage drug release and angle of repose of optimized DE liquisolid capsules were found to be 86.23 ± 1.37 and 36.08 ± 0.63, respectively. XRD studies indicated a reduction in the crystallinity of the drug in liquisolid formulations. The findings suggested that dabigatran etexilate’s solubility rate could be increased by using liquisolid capsules. ANN gave better predictability than the design of experiments.

Introduction

A strong synthetic non-peptide competitive thrombin inhibitor, dabigatran is a member of the Biopharmaceutical Classification System (BCS) class II. Due to its poor absorption following oral administration, it is provided as a prodrug. The anticoagulant dabigatran etexilate (DE) is inert. Following oral consumption, non-specific DE esterases in the liver and plasma activate this prodrug. The first direct thrombin inhibitor approved by the FDA is dabigatran etexilate, a fast-acting, low-molecular-weight, reversible medication used to treat atrial fibrillation-related stroke and systemic embolism and to prevent venous thromboembolism (VTE) following knee and hip surgery [1]. The solubility of DE is strongly influenced by pH, increasing at acidic pH levels. DE’s poor solubility and P-gp efflux result in low bioavailability (7.2%) after oral dosing. To improve the oral bioavailability of dabigatran etexilate, several works have been reported employing various formulation techniques, including the solid self-micro emulsifying drug delivery system [2], the drug-phospholipids complex nano-emulsion [3], and the Soluplus®-TPGS binary mixed micelles system [4].

Drug solubility plays a vital role in assessing its bioavailability. Nearly 70% of newly developed drugs and 40% of commercial novel pharmaceuticals in oral formulations have exhibited limited water solubility. Oral drug delivery is favored due to its advantages in patient compliance, convenience, and cost-effectiveness. For optimal absorption, orally administered drugs must be adequately dissolved in gastric fluids. Drugs that demonstrate low solubility in these fluids are less readily absorbed, leading to diminished bioavailability. The enhancement of drug dissolution performance and the development of formulations that ensure suitable bioavailability and therapeutic efficacy represent significant challenges currently confronting the pharmaceutical industry. Various methodologies, including micronization, nanonization, complexation with cyclodextrins, solid dispersion, self-emulsifying systems, and liquisolid systems, are under investigation to address these issues and improve dissolution performance and bioavailability [5,6].

A contemporary approach referred to as “powdered solution technology”, commonly known as “liquisolid technology”, has been employed to develop fast-acting solid dosage forms of drugs that are insoluble in water. The liquisolid process entails the combination of a liquid with a carrier and a coating agent to produce a free-flowing powder. Liquisolid systems consist of dry, non-adherent powder mixtures formed by integrating liquid drugs, suspensions, or solutions with carriers and coating materials in nonvolatile solvents [7]. Liquid medication is defined as a mixture of an insoluble substance and a solvent. A liquisolid formulation, which incorporates a solvent, is transformed into a dry, non-adhesive, and compressible powder by adding a diluent and a lubricant [8]. The process entails transforming a liquid or solid medication into a solubilized state. Ingesting a liquid that has been mixed with a drug facilitates its dissolution, while the incorporation of a carrier substance promotes absorption into the drug layer [9].

The medication that is solubilized in the nonsolvent is integrated into a carrier substance, which may include materials such as cellulose, Neusilin, or Fujicalin. This process facilitates both absorption and adsorption; initially, the liquid is absorbed within the particles, subsequently being retained by their internal framework. Following this initial phase, the adsorption of the liquid takes place on both the internal and external surfaces of the porous carrier particles once saturation is achieved. The essential flow properties of the liquisolid system are imparted by the coating material, which possesses high adsorptive capabilities and a substantial specific surface area. The liquisolid technique offers several advantages, including enhanced bioavailability, reduced production costs, regulated drug release, and consistent dissolution rates. A typical liquisolid formulation comprises the following key components: a non-volatile solvent, the drug candidate, carrier materials, coating materials, and a disintegrant [10].

Non-volatile solvents are characterized by their elevated boiling points. These solvents are inert organic systems that ideally possess water solubility and exhibit low viscosity. The development of liquisolid systems incorporates a range of nonvolatile solvents, including propylene glycol, polysorbate 80, glycerin, and Kolliphor EL. Medications encompass digoxin, digitoxin, dabigatran etexilate, prednisolone, hydrocortisone, spironolactone, hydrochlorothiazide, polythiazide, as well as various liquid formulations such as chlorpheniramine, water-insoluble vitamins, and fish oil, among others, which serve as examples of potential drug candidates. Liquid medications consist of lipophilic pharmaceuticals in liquid form, drug suspensions, and solutions containing solid, water-insoluble drugs dissolved in suitable nonvolatile solvent systems. Carrier materials typically consist of large, preferably porous particles that enhance compression and possess sufficient absorption capacity to facilitate liquid uptake.

Examples of such materials include various grades of cellulose, starch, lactose, sorbitol, Avicel PH 102 and 200, Eudragit RL and RS, as well as amorphous cellulose. Coating materials possess the ability to adsorb surplus liquid, consisting of exceptionally fine particles ranging from 10 nm to 5,000 nm in diameter. These highly adsorptive coating agents, which include various grades of silica such as Cab-O-Sil M5, Aerosil 200, and Syloid 244FP, enhance flow properties and assist in enveloping the wet carrier particles, thereby creating the illusion of a dry powder. Commonly utilized disintegrants include sodium starch glycolate (such as Explotab13 and Pumogel) and starch, which are among the most frequently employed in formulations [11].

The advantage of an organic solvent-free preparation procedure of liquisolid technique and other advantages are the reasons for the choice of liquisolid capsules of DE to increase the drug solubility. Liqusolid technique has attempted to increase the solubility of DE mesylate by Prasanthi et al. DE mesylate liquisolid compacts were made with a non-volatile solvent mixture of span 80 and castor oil, coating material, Aerosil 200 in various ratios (R = 5, 10, 15, 20, 25), and carrier materials such as maize starch, MCC, Avicel pH 101 and 102, and Prosolv SMCC 50 with loading factors of 0.72, 0.75, 0.77, 0.87, and 1.75, respectively [12]. In the past work, optimization of the formulation was done based on the trial-and-error method which is time-consuming and there is no assurance of obtaining the optimized formulation. There is no work reported on the optimization of liquisolid capsules of dabigatran etexilate. The current work aimed to optimize the DE liquisolid formulation using two systematic statistical approaches, experimental design and Artificial Neural Networks (ANN). The current study optimized DE Liquisolid capsules utilizing D-optimal design. The formulations’ angle of repose and percentage of drug release were measured and assessed. The optimized formulation was prepared and assessed.

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Materials

Dabigatran etexilate was purchased from Yarrow Chem Products (Mumbai, Maharashtra 400086). Polyethylene glycol (PEG 400) was purchased from Merck Life Science Private Limited India. Kolliphor® EL [Polyoxyl 35 Castor Oil (USP-NF)] was a generous gift from BASF (Mumbai, India). Neusilin® US2 (magnesium aluminometasilicate) was a generous gift from Sunkem Industries. Fujicalin® (spherically granulated dicalcium phosphate anhydrous) was purchased from Himedia laboratories. All other chemicals and solvents were of analytical grade.

Rama Devi Korni, Thanmaisree Bora, Akhil Majji, Jagadeesh Panda, Sre Meghna Killana, Liquisolid Technique For Solubility Enhancement of a Poorly Soluble Thrombin Inhibitor: Optimization Using Design of Experiments and Artificial Neural Networks, Received: 18.12.2025 / Revised: 06.02.2025 / Accepted: 01.03.2025 / Published: 03.10.2025, Prospects in Pharmaceutical Sciences, 23(3), 97-109
https://prospects.wum.edu.pl/