Physicochemical compatibility of caffeine citrate and caffeine base injections with parenteral medications used in neonatal intensive care settings.

PURPOSE: To investigate the physicochemical compatibility of caffeine citrate and caffeine base injections with 43 secondary intravenous (IV) drugs used in Neonatal Intensive Care Unit (NICU) settings. METHODS: Caffeine citrate (20 mg/mL or 10 mg/mL) or caffeine base injection (10 mg/mL) were mixed in a volume ratio of 1:1 with the secondary drug solution to simulate Y-site co-administration procedures in NICUs. Physical compatibility was evaluated based on visual observation for 2 h, against a black and white background and under polarised light, for changes in colour, precipitation, haze and evolution of gas. Chemical compatibility was determined from caffeine concentration measurements, using a validated high-performance liquid chromatography assay. RESULTS: Six of the 43 secondary drugs tested (aciclovir, amphotericin (liposomal), furosemide, hydrocortisone, ibuprofen and ibuprofen lysine) were physically incompatible with caffeine citrate undiluted injection (20 mg/mL), at their high-end, clinically relevant concentrations for NICU settings. However, when tested at lower concentrations, hydrocortisone (1 mg/mL) was physicochemically compatible, whereas furosemide (0.2 mg/mL) was physically incompatible with caffeine citrate. The six drugs which showed physical incompatibility with caffeine citrate 20 mg/mL injection were also physically incompatible with caffeine citrate 10 mg/mL solution. All 43 secondary drugs tested were physicochemically compatible with caffeine base injection. CONCLUSIONS: Most secondary test drugs, except aciclovir, amphotericin (liposomal), furosemide, hydrocortisone, ibuprofen and ibuprofen lysine, were physicochemically compatible with caffeine citrate injection. Caffeine base injection was physicochemically compatible with all 43 test drugs tested.

Could late-latent syphilis be treated with a single subcutaneous infusion of long-acting penicillin?

Syphilis is an important global health threat and little has changed in its treatment since the mid-20th century. For late-latent or syphilis infection of unknown duration, the standard treatment of multiple intramuscular injections of benzathine penicillin G (BPG) are associated with significant pain and distress to clients and caregivers, negatively impacting on treatment completion. Based on pharmacokinetic modelling from a Phase I study of subcutaneous infusion of high dose BPG (SCIP), we present its feasibility, safety and tolerability for treatment of syphilis in a single infusion. SCIP leads to more sustained penicillin concentrations above the desired target with less reported pain and reduced clinic visits.

The Physicochemical Compatibility of Sildenafil Injection with Parenteral Medications Used in Neonatal Intensive Care Settings.

Sildenafil is used to treat pulmonary hypertension in neonatal intensive care unit (NICU) settings. As multiple intravenous (IV) medications are co-administered in NICU settings, we sought to investigate the physicochemical compatibility of sildenafil with a range of IV drugs. Sildenafil 600 mcg/mL or 60 mcg/mL was mixed 1:1 with the secondary drug solution to simulate Y-site co-administration procedures. Physical compatibility was evaluated by visual observation against a black and white background and under polarized light for two hours for changes in colour, precipitation, haze and evolution of gas. Chemical compatibility was determined from sildenafil concentrations, using a validated, stability-indicating high-performance liquid chromatography assay. Sildenafil 600 mcg/mL was physicochemically compatible with 29 of the 45 drugs tested at 'high-end' clinical concentrations and physically incompatible with 16 drugs and six '2-in-1' parenteral nutrition solutions. Sildenafil 600 mcg/mL was compatible with lower, clinically relevant concentrations of calcium gluconate, heparin and hydrocortisone. Aciclovir, amoxicillin, ampicillin, ibuprofen lysine, indometacin, phenobarbitone and rifampicin were incompatible with sildenafil 600 mcg/mL, however these IV medications were compatible with sildenafil 60 mcg/mL. Sildenafil 600 mcg/mL and 60 mcg/mL were incompatible with amphotericin, flucloxacillin, furosemide, ibuprofen, meropenem and sodium bicarbonate. Sildenafil compatibility with commonly used syringe filters was also investigated. Sildenafil solution was compatible with nylon syringe filters, however, absorption/adsorption loss occurred with polyethersulfone and cellulose ester filters.

Development of a pharmaceutical science systematic review process using a semi-automated machine learning tool: Intravenous drug compatibility in the neonatal intensive care setting.

Our objective was to establish and test a machine learning-based screening process that would be applicable to systematic reviews in pharmaceutical sciences. We used the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) model, a broad search strategy, and a machine learning tool (Research Screener) to identify relevant references related to y-site compatibility of 95 intravenous drugs used in neonatal intensive care settings. Two independent reviewers conducted pilot studies, including manual screening and evaluation of Research Screener, and used the kappa-coefficient for inter-reviewer reliability. After initial deduplication of the search strategy results, 27 597 references were available for screening. Research Screener excluded 1735 references, including 451 duplicate titles and 1269 reports with no abstract/title, which were manually screened. The remainder (25 862) were subject to the machine learning screening process. All eligible articles for the systematic review were extracted from <10% of the references available for screening. Moderate inter-reviewer reliability was achieved, with kappa-coefficient ≥0.75. Overall, 324 references were subject to full-text reading and 118 were deemed relevant for the systematic review. Our study showed that a broad search strategy to optimize the literature captured for systematic reviews can be efficiently screened by the semi-automated machine learning tool, Research Screener.