Machine learning in accelerating microsphere formulation development.

Microspheres have gained much attention from pharmaceutical and medical industry due to the excellent biodegradable and long controlled-release characteristics. However, the drug release behavior of microspheres is influenced by complicated formulation and manufacturing factors. The traditional formulation development of microspheres is intractable and inefficient by the experimentally trial-and-error methods. This research aims to build a prediction model to accelerate microspheres product development for small-molecule drugs by machine learning (ML) techniques. Two hundred eighty-six microsphere formulations with small-molecule drugs were collected from the publications and pharmaceutical company, including the dissolution temperature at both 37 ℃ and 45 ℃. After the comparison of fourteen ML approaches, the consensus model achieved accurate predictions for the validation set at 37 ℃ and 45 ℃ (R2 = 0.880 vs. R2 = 0.958), indicating the good performance to predict the in vitro drug release profiles at both 37 ℃ and 45 ℃. Meanwhile, the models revealed the feature importance of formulations, which offered meaningful insights to the microspheres development. Experiments of microsphere formulations further validated the accuracy of the consensus model. Furthermore, molecular dynamics (MD) simulation provided a microscopic view of the preparation process of microspheres. In conclusion, the prediction model of microsphere formulations for small-molecule drugs was successfully built with high accuracy, which is able to accelerate microspheres product development and promote the quality control of microspheres for the pharmaceutical industry.

Integrated in silico formulation design of self-emulsifying drug delivery systems.

The drug formulation design of self-emulsifying drug delivery systems (SEDDS) often requires numerous experiments, which are time- and money-consuming. This research aimed to rationally design the SEDDS formulation by the integrated computational and experimental approaches. 4495 SEDDS formulation datasets were collected to predict the pseudo-ternary phase diagram by the machine learning methods. Random forest (RF) showed the best prediction performance with 91.3% for accuracy, 92.0% for sensitivity and 90.7% for specificity in 5-fold cross-validation. The pseudo-ternary phase diagrams of meloxicam SEDDS were experimentally developed to validate the RF prediction model and achieved an excellent prediction accuracy (89.51%). The central composite design (CCD) was used to screen the best ratio of oil-surfactant-cosurfactant. Finally, molecular dynamic (MD) simulation was used to investigate the molecular interaction between excipients and drugs, which revealed the diffusion behavior in water and the role of cosurfactants. In conclusion, this research combined machine learning, central composite design, molecular modeling and experimental approaches for rational SEDDS formulation design. The integrated computer methodology can decrease traditional drug formulation design works and bring new ideas for future drug formulation design.

Integrated computer-aided formulation design: A case study of andrographolide/ cyclodextrin ternary formulation.

Current formulation development strongly relies on trial-and-error experiments in the laboratory by pharmaceutical scientists, which is time-consuming, high cost and waste materials. This research aims to integrate various computational tools, including machine learning, molecular dynamic simulation and physiologically based absorption modeling (PBAM), to enhance andrographolide (AG) /cyclodextrins (CDs) formulation design. The lightGBM prediction model we built before was utilized to predict AG/CDs inclusion's binding free energy. AG/γ-CD inclusion complexes showed the strongest binding affinity, which was experimentally validated by the phase solubility study. The molecular dynamic simulation was used to investigate the inclusion mechanism between AG and γ-CD, which was experimentally characterized by DSC, FTIR and NMR techniques. PBAM was applied to simulate the in vivo behavior of the formulations, which were validated by cell and animal experiments. Cell experiments revealed that the presence of D-α-Tocopherol polyethylene glycol succinate (TPGS) significantly increased the intracellular uptake of AG in MDCK-MDR1 cells and the absorptive transport of AG in MDCK-MDR1 monolayers. The relative bioavailability of the AG-CD-TPGS ternary system in rats was increased to 2.6-fold and 1.59-fold compared with crude AG and commercial dropping pills, respectively. In conclusion, this is the first time to integrate various computational tools to develop a new AG-CD-TPGS ternary formulation with significant improvement of aqueous solubility, dissolution rate and bioavailability. The integrated computational tool is a novel and robust methodology to facilitate pharmaceutical formulation design.

Super-Necking Crystal Growth and Structural and Magnetic Properties of SrTb2O4 Single Crystals.

We report on single-crystal growths of the SrTb2O4 compound by a super-necking technique with a laser-floating-zone furnace and study the stoichiometry, growth mode, and structural and magnetic properties by scanning electronic microscopy, neutron Laue, X-ray powder diffraction, and the physical property measurement system. We optimized the growth parameters, mainly the growth speed, atmosphere, and the addition of a Tb4O7 raw material. Neutron Laue diffraction displays the characteristic feature of a single crystal. Our study reveals an atomic ratio of Sr:Tb = 0.97(2):2.00(1) and a possible layer by layer crystal growth mode. Our X-ray powder diffraction study determines the crystal structure, lattice constants, and atomic positions. The paramagnetic (PM) Curie-Weiss (CW) temperature θCW = 5.00(4) K, and the effective PM moment M mea eff = 10.97(1) µB per Tb3+ ion. The data of magnetization versus temperature can be divided into three regimes, showing a coexistence of antiferromagnetic and ferromagnetic interactions. This probably leads to the magnetic frustration in the SrTb2O4 compound. The magnetization at 2 K and 14 T originates from both the Tb1 and Tb2 sites and is strongly frustrated with an expected saturation field at ∼41.5 T, displaying an intricate phase diagram with three ranges.

Lacidipine Amorphous Solid Dispersion Based on Hot Melt Extrusion: Good Miscibility, Enhanced Dissolution, and Favorable Stability.

The present study aimed to increase the in vitro dissolution rate of lacidipine, a poorly water-soluble drug, by formulating amorphous solid dispersions (ASDs) using hot-melt extrusion (HME). Differential scanning calorimetry, powder X-ray diffraction, polarized light microscopy, and Fourier transform infrared were used to characterize the optimal formulations and evaluate the physical stability for the stress test. Film-casting method and hot-stage microscopy were applied to study the miscibility of lacidipine and the drug carriers. In vitro dissolution tests were conducted as the final evaluation index. The optimal formulations were successfully obtained with Soluplus and PVP VA64 at a drug/carrier ratio of 1:10 (w/w), Fourier transform infrared studies revealed the hydrogen bonding between drug and polymers, and in vitro dissolution rates of the optimal formulations were extremely enhanced compared to bulk lacidipine and physical mixtures, similar with that of the commercial tablet. The ASD formulated with Soluplus showed better physical stability than that with PVP VA64. A strong hydrogen bonding and good drug-polymer miscibility were essential to hinder the recrystallization of lacidipine ASDs. In conclusion, the lacidipine ASD formulated with Soluplus showed a significant increase in in vitro dissolution rate and favorable physical stability in the stress test.

Evaluation of Intestinal Absorption and Bioavailability of a Bergenin-Phospholipid Complex Solid Dispersion in Rats.

Bergenin (BN) is a Biopharmaceutics Classification System class IV (BCS IV) drug with poor hydrophilicity and lipophilicity and is potentially eliminated by the efflux function of P-glycoprotein (P-gp). These factors may explain its low oral bioavailability. In the present study, a BN-phospholipid complex solid dispersion (BNPC-SD) was prepared by solvent evaporation and characterized based on differential scanning calorimetry, powder X-ray diffraction, scanning electron microscopy, infrared diffraction, solubility, octanol-water partition coefficient, and in vitro dissolution. To investigate how P-gp can inhibit BN absorption in vivo, the P-gp inhibitor verapamil was co-administered with BNPC-SD to Sprague Dawley rats. By in situ single-pass intestinal perfusion, the membrane permeability of BN from BNPC-SD was higher than that of BN given alone and was improved further by co-administered verapamil. A pharmacokinetics study was done in Sprague Dawley rats, with plasma BN levels estimated by high-performance liquid chromatography. Cmax and AUC0 → t values for BN were significantly higher for BNPC-SD than for BN given alone and were increased further by verapamil. Thus, the relative oral bioavailability of BNPC-SD as well as BNPC-SD co-administered with verapamil was 156.33 and 202.46%, respectively, compared with the value for BN given alone. These results showed that BNPC-SD can increase the oral bioavailability of BCS IV drugs.

Determination of quantitative retention-activity relationships between pharmacokinetic parameters and biological effectiveness fingerprints of Salvia miltiorrhiza constituents using biopartitioning and microemulsion high-performance liquid chromatography.

In this study, we analyzed danshen (Salvia miltiorrhiza) constituents using biopartitioning and microemulsion high-performance liquid chromatography (MELC). The quantitative retention-activity relationships (QRARs) of the constituents were established to model their pharmacokinetic (PK) parameters and chromatographic retention data, and generate their biological effectiveness fingerprints. A high-performance liquid chromatography (HPLC) method was established to determine the abundance of the extracted danshen constituents, such as sodium danshensu, rosmarinic acid, salvianolic acid B, protocatechuic aldehyde, cryptotanshinone, and tanshinone IIA. And another HPLC protocol was established to determine the abundance of those constituents in rat plasma samples. An experimental model was built in Sprague Dawley (SD) rats, and calculated the corresponding PK parameterst with 3P97 software package. Thirty-five model drugs were selected to test the PK parameter prediction capacities of the various MELC systems and to optimize the chromatographic protocols. QRARs and generated PK fingerprints were established. The test included water/oil-soluble danshen constituents and the prediction capacity of the regression model was validated. The results showed that the model had good predictability.

Establishment of quantitative retention-activity model by optimized microemulsion liquid chromatography.

The effective permeability coefficient is of theoretical and practical importance in evaluation of the bioavailability of drug candidates. However, most methods currently used to measure this coefficient are expensive and time-consuming. In this paper, we addressed these problems by proposing a new measurement method which is based on the microemulsion liquid chromatography. First, the parallel artificial membrane permeability assays model was used to determine the effective permeability of drug so that quantitative retention-activity relationships could be established, which were used to optimize the microemulsion liquid chromatography. The most effective microemulsion system used a mobile phase of 6.0% (w/w) Brij35, 6.6% (w/w) butanol, 0.8% (w/w) octanol, and 86.6% (w/w) phosphate buffer (pH 7.4). Next, support vector machine and back-propagation neural networks are employed to develop a quantitative retention-activity relationships model associated with the optimal microemulsion system, and used to improve the prediction ability. Finally, an adequate correlation between experimental value and predicted value is computed to verify the performance of the optimal model. The results indicate that the microemulsion liquid chromatography can serve as a possible alternative to the PAMPA method for determination of high-throughput permeability and simulation of biological processes.