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aripiprazole (Abilify Maintena / Lu AF41155 / aripiprazole ERIS)

✓ Approved

Otsuka Holdings Co., Ltd. · DRD2 · Small Molecule

What is aripiprazole?

aripiprazole is a small molecule developed by Otsuka Holdings Co., Ltd.. It is approved for therapeutic indications via injectable (others) or intramuscular (im) injection.

Drug Profile

Brand NamesAbilify Maintena, Lu AF41155, aripiprazole ERIS
CompanyOtsuka Holdings Co., Ltd.
Drug ClassSmall Molecule
Molecular TargetDRD2, HTR1A, HTR2A
RouteInjectable (Others), Intramuscular (IM) Injection
StatusApproved
Sources: ClinicalTrials.gov, Otsuka Holdings Co., Ltd.

Mechanism of Action

Molecular Targets

aripiprazole acts on 3 molecular targets:

DRD2dopamine receptor D2 (D2DR, D2R)
HTR1A5-hydroxytryptamine receptor 1A (PFMCD, 5-HT1A)
HTR2A5-hydroxytryptamine receptor 2A (5-HT2A, HTR2)
Want deeper analysis?Noah AI can explain complex mechanisms and compare to similar drugs.

Therapeutic Indications

aripiprazole is developed for 2 unique indications across 1 therapeutic area.

Therapeutic AreaConditionPhase
Psychiatric disordersSchizophrenia✓ Approved
Psychiatric disordersBipolar disorder✓ Approved

Related Research Articles

PubMedJournal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research2026-05-24

Vertebral fractures in a young woman: the impact of hypogonadism, glucocorticoids and chronic disease.

Herath Madhuni M, Nguyen Hanh H HH, Milat Frances F, Ebeling Peter R PR

A premenopausal woman was reviewed for painful vertebral fractures in the context of prednisolone exposure for her newly diagnosed systemic lupus erythematosus. Known clinical risk factors for bone loss included exposure to depot medroxyprogesterone acetate for 12 yr, cigarette smoking, a family history of osteoporosis and ongoing inflammatory arthritis. Initial investigations also identified vitamin D deficiency and low bone mass for age and sex. She was treated with vitamin D and her contraception was changed to a levonorgestrel intrauterine device. In the context of further vertebral fractures confirmed with both MRI and bone scan and having had additional secondary causes of low bone mass excluded, she commenced targeted osteoporosis treatment. We discuss the complexities of managing bone fragility in young adults and the impact of depot medroxyprogesterone acetate, inflammatory disease (systemic lupus erythematosus) and glucocorticoid-induced osteoporosis on younger adults. In this woman, antiresorptive therapy with zoledronic acid was recommended; we also explore the existing evidence-base for antiresorptive and anabolic therapies in younger adults, with a particular focus on glucocorticoid-induced osteoporosis.

PMID 42176199
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PubMedJournal of biopharmaceutical statistics2026-05-24

An exploratory application of modern statistical methodology and machine learning techniques in assessment of crystallinity monitoring and control strategy development for high-risk drug manufacturing.

Chen Kevin L KL, Chen Yingjie Y, Wu Huiquan H

Pharmaceutical polymorphism and crystallinity changes may have significant impact on drug's quality, efficacy, and safety. The risk mitigation strategies include pharmaceutical development, monitoring and control strategy establishment during manufacturing, and stability monitoring program during storage. All of those are resources intensive. During data mining and analysis of risk mitigation strategies of new drug applications (NDAs) and abbreviated new drug applications (ANDAs) which may involve polymorphism and crystallinity change, one of the challenges is data heterogeneity encountered in submissions. This data heterogeneity may result in data not readily available for automated analysis which is called missing data. Modern statistical methodologies are available to handle this missing data and associated data analyses; however, very limited deployment of these methods to pharmaceutical chemistry, manufacturing, and controls (CMC) regulatory domain being reported. In big data era, consideration of statistical methodologies in this field will become continuously more important as the amount of available data in regulatory submissions increases. In this study, through data mining of approved NDAs and ANDAs by the FDA during the years 2017-2022 which had polymorphism and/or crystallinity keywords, we established a dataset which contained 148 approved NDAs and ANDAs and involved crystallinity monitoring and control strategy development of high-risk drug product manufacturing processes. Then, we applied several advanced machine learning techniques for exploratory pattern recognition and risk classification in the pharmaceutical manufacturing CMC domain. Furthermore, we conducted Monte Carlo simulations to demonstrate the feasibility of risk classification with generated synthetic outcomes using supervised machine learning techniques to the dataset established.

PMID 42177643
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PubMedScientific reports2026-05-24

Integrating multivariate analysis and Air Pollution Tolerance Index (APTI) to evaluate four ornamental plants for sustainable indoor air phytoremediation.

Elhadad Safinaz M SM, Ea Shalaby S, Saleh Ibrahim H IH, Omar Mohamed Y MY

Indoor air pollution, especially in pharmaceutical laboratories, poses significant health risks due to the presence of volatile organic compounds (VOCs) such as benzene, toluene, acetophenone, and benzaldehyde. This study evaluates the efficiency of air phytoremediation technology using four ornamental plant species, Cordyline fruticosa, Syngonium podophyllum, Epipremnum aureum and Chlorophytum comosum to improve Indoor Air Quality (IAQ) by acting as Plant-Based Bio-Filters (PBBFs) in both pot-based and green wall configurations. VOC concentrations were monitored in a real pharmaceutical organic laboratory. Morphological and physiological plant traits including total chlorophyll content, relative water content (RWC), leaf pH, ascorbic acid concentration, stomatal density, and cuticle wax content were evaluated. Air Pollution Tolerance Index (APTI) and dust-capturing potential were calculated to assess the resilience and effectiveness of each species under VOCs exposure. Chemometric tools Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures-Discriminant Analysis (OPLS-DA) were applied to identify species with superior removal efficiency and to explore the relationship between plant traits and VOC uptake. Among the studied species, Cordyline fruticosa demonstrated the highest removal efficiency for VOCs (87.50%), CO (88.23%), and CO₂ (36.78%), as well as the highest APTI (14.76%), stomatal density (94.34 stomata/mm2), and chlorophyll content. Syngonium podophyllum also showed up to 100% removal of particulate matter (PM2.5 and PM10) and performed effectively in CO (70.58%) and CO₂ (31.27%) reduction. Multivariate analysis confirmed that plants with higher physiological resilience and morphological surface complexity had significantly greater phytoremediation capacity. This study demonstrate the potential of PBBFs, especially using Cordyline fruticosa and Syngonium podophyllum, as a viable, cost-effective, and sustainable approach to mitigate indoor VOCs and improve air quality in pharmaceutical labs. The findings support integrating ornamental plants into indoor environment as a natural solution for IAQ management.

PMID 42177233
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PubMedElectrophoresis2026-05-24

Determination of Flunixin Meglumine in Veterinary Pharmaceutical Formulations by Capillary Electrophoresis With Capacitively Coupled Contactless Conductivity Detection.

de Araujo Letícia M LM, da Silva José A Fracassi JAF, de Jesus Dosil P DP

This work describes a novel, simple, fast, reliable, and environmentally friendly analytical method for the determination of the anti-inflammatory and analgesic drug flunixin meglumine (FM) in veterinary pharmaceutical formulations. The method uses capillary electrophoresis with capacitively coupled contactless conductivity detection (CE-C4D) to quantify meglumine (N-methylglucamine), the counterion of flunixin in FM. This approach allows the use of the less expensive and more readily available meglumine, rather than FM or flunixin, as the standard reagent. Lithium ions were used as an internal standard, and the migration time of the meglumine was 2.2 min using a capillary with a total length of 50 cm (42 cm effective) and 50 µm (i.d.), filled with a background electrolyte (BGE) composed of 2-(N-morpholino)ethanesulfonic acid (MES) and histidine (His), each at 20 mmol L-1. Good linearity was attained (R2 = 0.992) in a concentration range of 100-2500 µg mL-1. The limits of detection (LOD) and quantification (LOQ) were calculated as 22.7 and 75.6 µg mL-1, respectively. The accuracy of the method was assessed using recovery tests at three concentration levels, resulting in recoveries ranging from 89% to 104%. The intra- and inter-day precisions ranged from 2.3% to 4.2%. The proposed CE-C4D was successfully applied to determine FM in commercial veterinary formulations.

PMID 42175833
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PubMedScientific reports2026-05-24

Impact of clinical pharmacist interventions on drug-related problems in hospitalised patients with renal dysfunction: a quasi-experimental study.

Özmen Özge Ö, Bektay Muhammed Yunus MY, Karatoprak Cumali C, İzzettin Fikret Vehbi FV

Patients with renal dysfunction are highly susceptible to drug-related problems (DRPs) due to altered pharmacokinetics and polypharmacy. Clinical pharmacists(CP) play a key role in optimising pharmacotherapy and preventing medication errors in this vulnerable population. To evaluate the prevalence of DRPs in patients with renal dysfunction and to compare the effectiveness of a stepwise clinical pharmacy model-transitioning from passive education (Phase 2) to active multidisciplinary integration (Phase 3)-in optimizing medication safety within a Turkish tertiary-care setting. This prospective, quasi-experimental study was conducted in the Internal Medicine Ward of a tertiary-care hospital in Istanbul, Türkiye, between November 2021-2022. The study included adult patients(≥ 18 years) with estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 and at least one medication requiring renal dose adjustment. Three consecutive study phases implemented: observation, education, and intervention. DRPs were identified and classified using the Pharmaceutical Care Network Europe (PCNE) V9.1 classification system. CP interventions were documented, and their acceptance and outcomes were evaluated. A total of 160 patients were included (mean age 74.5 ± 10.1 years). The prevalence of DRPs decreased progressively across the three phases (238, 195, and 169 DRPs, respectively). DRPs associated with renal dysfunction declined significantly from 61.0% to 38.0% of patients (p = 0.046). The most frequent categories were treatment safety (≈70%) and inappropriate drug selection or dosing. In the intervention phase, 47.3% of identified DRPs received active pharmacist interventions, of which 98.7% were accepted and 91.7% fully implemented. Our results demonstrates that while passively educating the medical team provides limited benefit, the active integration of CP into multidisciplinary care is essential for significantly reducing DRPs and improving medication safety in patients with renal dysfunction. The exceptionally high acceptance of these interventions underscores the critical value of real-time, collaborative pharmaceutical care in optimising renal pharmacotherapy.

PMID 42177226
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PubMedMolecular pharmaceutics2026-05-24

Platelets as Programmable Drug Depots: Toward Predictive and Mechanistic Drug Loading.

Moskalensky Alexander E AE

Efficient drug delivery remains a major challenge in pharmaceutical science, with synthetic nanocarriers often facing limitations in real biological systems. While blood platelets have been explored as biomimetic carriers, their intrinsic capacity for molecular storage and stimulus-triggered release remains largely underutilized. Here, we propose that platelet granules can be viewed as programmable drug depots, whose loading can be rationally controlled using physicochemical principles and endogenous transport mechanisms. In contrast to empirical approaches, we outline a framework based on two complementary pathways: passive accumulation driven by ion trapping of weakly basic compounds in acidic granules, and active uptake mediated by vesicular transporters. Building on recent advances in structural biology and computational modeling, we further argue that drug loading into platelets can be predicted and optimized using molecular descriptors, docking approaches, and machine learning models to estimate transporter compatibility and accumulation efficiency. This perspective introduces a conceptual design loop linking prediction and molecular optimization─i.e., enabling both selection of suitable drug candidates and rational modification of their structures to enhance platelet compatibility. Together, these concepts position platelets not simply as biomimetic carriers but as model-guided and engineerable platforms for drug delivery, bridging biological systems with rational design strategies.

PMID 42175967
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