Development and characterization of ceftriaxone in-situ gel-forming biodegradable parenteral depot system

Purpose: To design parenteral in-situ gel of ceftriaxone using poloxamer as a thermosensitive agent, Carbopol as a pH-sensitive polymer and hydroxypropyl methylcellulose as a viscosity enhancer. Method: Lyophilized ceftriaxone was added in solution form to enhance its solubility and stability. Several formulations were designed using poloxamer (P 188, F 127 and P 407) and Carbopol (934P and 940) in varying concentrations, out of which an optimized formulation was chosen on the basis of its gelling capacity and respective transit time. Drug content uniformity, sterility and stability were studied. Drug-polymer and polymer-polymer interaction were determined by differential scanning calorimetry (DSC). Characterization of optimized formulation was carried out by Fourier transform infrared spectroscopy (FTIR). In-vitro release profile was determined by a modified Franz diffusion method. Results: Optimized formulation Q2 was characterized for various physicochemical parameters and found to be stable. In-vitro release study showed first order release pattern. DSC thermograms revealed that the polymers were compatible with each other as no physicochemical interactions were observed. The results were expressed as mean ± standard deviation (SD, p ≤ 0.05). Conclusion: Optimized formulation Q2 provided sustained release up to 10 days following first order release kinetics, and thus can be further developed for large-scale production

Enhanced Solubility and Stability of Aripiprazole in Binary and Ternary Inclusion Complexes Using Hydroxy Propyl Beta Cyclodextrin (HPβCD) and L-Arginine

The low water solubility of an active pharmaceutical ingredient (aripiprazole) is one of the most critical challenges in pharmaceutical research and development. This antipsychotic drug has an inadequate therapeutic impact because of its minimal and idiosyncratic oral bioavailability to treat schizophrenia. The main objective of this study was to improve the solubility and stability of the antipsychotic drug aripiprazole (ARP) via forming binary as well as ternary inclusion complexes with hydroxypropyl-β-cyclodextrin (HPβCD) and L-Arginine (LA) as solubility enhancers. Physical mixing and lyophilization were used in different molar ratios. The developed formulations were analyzed by saturation solubility analysis, and dissolution studies were performed using the pedal method. The formulations were characterized by FTIR, XRD, DSC, SEM, and TGA. The results showcased that the addition of HPβCD and LA inclusion complexes enhanced the stability, in contrast to the binary formulations and ternary formulations prepared by physical mixing and solvent evaporation. Ternary formulation HLY47 improved dissolution rates by six times in simulated gastric fluid (SGF). However, the effect of LA on the solubility enhancement was concentration-dependent and showed optimal enhancement at the ratio of 1:1:0.27. FTIR spectra showed the bond shifting, which confirmed the formation of new complexes. The surface morphology of complexes in SEM studies showed the rough surface of lyophilization and solvent evaporation products, while physical mixing revealed a comparatively crystalline surface. The exothermic peaks in DSC diffractograms showed diminished peaks previously observed in the diffractogram of pure drug and LA. Lyophilized ternary complexes displayed significantly enhanced thermal stability, as observed from the thermograms of TGA. In conclusion, it was observed that the preparation method and a specific drug-to-polymer and amino acid ratio are critical for achieving high drug solubility and stability. These complexes seem to be promising candidates for novel drug delivery systems development

Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning

Abstract Background Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED. Methods This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S. institution. A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression. Results Overall ICC was 0.820 (95% CI 0.790–0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861–0.920) for the neural network and 0.936 (95% CI 0.918–0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906). Conclusion The deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management