Neural network-assisted humanisation of COVID-19 hamster transcriptomic data reveals matching severity states in human disease.

BACKGROUND: Translating findings from animal models to human disease is essential for dissecting disease mechanisms, developing and testing precise therapeutic strategies. The coronavirus disease 2019 (COVID-19) pandemic has highlighted this need, particularly for models showing disease severity-dependent immune responses. METHODS: Single-cell transcriptomics (scRNAseq) is well poised to reveal similarities and differences between species at the molecular and cellular level with unprecedented resolution. However, computational methods enabling detailed matching are still scarce. Here, we provide a structured scRNAseq-based approach that we applied to scRNAseq from blood leukocytes originating from humans and hamsters affected with moderate or severe COVID-19. FINDINGS: Integration of data from patients with COVID-19 with two hamster models that develop moderate (Syrian hamster, Mesocricetus auratus) or severe (Roborovski hamster, Phodopus roborovskii) disease revealed that most cellular states are shared across species. A neural network-based analysis using variational autoencoders quantified the overall transcriptomic similarity across species and severity levels, showing highest similarity between neutrophils of Roborovski hamsters and patients with severe COVID-19, while Syrian hamsters better matched patients with moderate disease, particularly in classical monocytes. We further used transcriptome-wide differential expression analysis to identify which disease stages and cell types display strongest transcriptional changes. INTERPRETATION: Consistently, hamsters' response to COVID-19 was most similar to humans in monocytes and neutrophils. Disease-linked pathways found in all species specifically related to interferon response or inhibition of viral replication. Analysis of candidate genes and signatures supported the results. Our structured neural network-supported workflow could be applied to other diseases, allowing better identification of suitable animal models with similar pathomechanisms across species. FUNDING: This work was supported by German Federal Ministry of Education and Research, (BMBF) grant IDs: 01ZX1304B, 01ZX1604B, 01ZX1906A, 01ZX1906B, 01KI2124, 01IS18026B and German Research Foundation (DFG) grant IDs: 14933180, 431232613.

Solving selectivity issues in LBAs: case study using Gyrolab to quantify CB307, a bispecific Humabody in human serum.

Aim: Endogenous interferents can cause nonselectivity in ligand binding pharmacokinetic assays, leading to inaccurate quantification of drug concentrations. We describe the development of a Gyrolab immunoassay to quantify a new modality, CB307 and discuss strategies implemented to overcome matrix effects and achieve selectivity at the desired sensitivity.Results: Matrix effects were mitigated using strategies including increasing minimum required dilution (MRD) and lower limit of quantification, optimization of antibody orientation, assay buffer and solid phase.Conclusion: The strategies described resulted in a selective method for CB307 in disease state matrix that met bioanalytical method validation (BMV) guidance and is currently used to support clinical pharmacokinetic sample analysis in the first-in-human POTENTIA clinical study (NCT04839991) as a secondary clinical end point.

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Impact of the diseased lung microenvironment on the in vivo fate of inhaled particles.

Inhalation drug delivery is superior for local lung disease therapy. However, there are several unique absorption barriers for inhaled drugs to overcome, including limited drug deposition at the target site, mucociliary clearance, pulmonary macrophage phagocytosis, and systemic exposure. Moreover, the respiratory disease state can affect or even destroy the physiology of the lung, thus influencing the in vivo fate of inhaled particles compared with that in healthy lungs. Nevertheless, limited information is available on this effect. Thus, in this review, we present pathological changes of the lung microenvironment under varied respiratory diseases and their influence on the in vivo fate of inhaled particles; such insights could provide a basis for rational inhalation particle design based on specific disease states.

Telehealth Pharmacist Approach to Comprehensive Medication Management in Post-Discharge High-Risk Patients: A Quality Improvement Initiative.

Introduction: Clinical Pharmacist-led Comprehensive Medication Management (CMM) has the potential to mitigate medication errors during transitions in care, but current evidence is underdeveloped. The objective of this work was to assess the impact of optimized CMM services through a telehealth pharmacist clinic on hospital readmission and Emergency Department (ED) utilization rates. Methods: A quality improvement study with patients discharged home from an urban, nonacademic Hospital in Westchester County, New York, receiving telehealth CMM was used. Participants included adult patients discharged home from an internal medicine unit considered high risk for preventable adverse medication errors based on comorbidities and prescribed medications. Eligible patients were offered to enroll in telehealth CMM visits with a clinical pharmacist immediately, 30 days, and 60 days post-discharge versus the current standard of care. Results: Primary outcomes included the impact on 30- and 90-day readmission and ED visit rates. Secondary outcomes included quantifying the outcomes on patient engagement, enrollment, and volume resulting from the program's process improvements. In this study, 3,060 patients were discharged from June 14, 2021, to May 10, 2022; 1,547 were eligible and offered CMM visits, and 889 completed enrollment (Treated). There was a 2.1% absolute difference in 30-day readmission rates between untreated and attempted (p = 0.07), and a 2.9% difference between the untreated and treated group (p = 0.04). Thirty-day ED utilization decreased by 1.6% between untreated and attempted (p = 0.3), and 3.5% between the untreated and treated (p = 0.03). There were four Plan-Do-Study-Act cycles in this program, in which the process improvements resulted in an overall average increase in patient volume, enrollment rates, and patient engagement for this QI initiative. Conclusions: This study yielded significant reductions in readmission and ED utilization rates among treated patients, highlighting successful process improvements that improved patient engagement and the potential for enhancing care coordination in vulnerable populations.

Collaborative inpatient and ambulatory care topic discussions in experiential education: An approach to assessing the Pharmacists' patient care process and transitions of care.

INTRODUCTION: The Pharmacists' Patient Care Process (PPCP) is woven throughout the doctor of pharmacy program; however, advanced pharmacy practice experiences (APPEs) are not structured in a specific order allowing students to apply the PPCP across the care continuum. Two faculty preceptors from two practice sites, inpatient family medicine and ambulatory care, joined to provide twice weekly, preceptor-led collaborative topic discussions to bridge this gap. This paper describes an approach to improve students' knowledge by incorporating progressive cases and assessments highlighting care across the continuum aligned with the PPCP. METHODS: Preceptors developed topic discussions, progressive cases, and assessments to emphasize the PPCP and transitions of care (TOC) during month-long APPEs. Students' perceptions were assessed through an anonymous Likert-type item survey. To assess learning, students completed anonymous, preceptor-developed, multiple-choice pre- and post-assessments aligned with the different components of the PPCP and TOC. RESULTS: In two academic years (2021-2023), 24 perception surveys were distributed and 21 were received from 19 unique students (87.5%). Compared to pre-implementation surveys, a significant increase was seen in the students strongly agreeing the incorporation of progressive cases improved their ability to apply the PPCP to patient care (48% vs. 76%, P value = .06). Twenty-four pre- and post-assessments (100%) were completed by 22 unique students, showing a statistically significant difference in students' understanding of content (71.8% vs. 81.6%, P value = .02). CONCLUSIONS: The incorporation of collaborative topic discussions for APPE students between inpatient and ambulatory care preceptors increased students' knowledge to apply the PPCP during TOC.

Renal Pharmacokinetic Adaptation to Cholestasis Causes Increased Nephrotoxic Drug Accumulation by Mrp6 Downregulation in Mice.

In hepatic dysfunction, renal pharmacokinetic adaptation can be observed, although information on the changes in drug exposure and the interorgan regulation of membrane transporters in kidney in liver diseases is limited. This study aimed to clarify the effects of renal exposure to nephrotoxic drugs during cholestasis induced by bile duct ligation (BDL). Among the 11 nephrotoxic drugs examined, the tissue accumulation of imatinib and cisplatin in kidney slices obtained from mice 2 weeks after BDL operation was higher than that in sham-operated mice. The uptake of imatinib in the kidney slices of BDL mice was slightly higher, whereas its efflux from the slices was largely decreased compared to that in sham-operated mice. Proteomic analysis revealed a reduction in renal expression of the efflux transporter multidrug resistance-associated protein 6 (Mrp6/Abcc6) in BDL mice, and both imatinib and cisplatin were identified as Mrp6 substrates. Survival probability after cisplatin administration was reduced in BDL mice. In conclusion, the present study demonstrated that BDL-induced cholestasis leads to the downregulation of the renal basolateral efflux transporter Mrp6, resulting in drug accumulation in renal cells and promoting drug-induced renal injury.

Impact of Disease States on the Oral Pharmacokinetics of EIDD-1931 (an Active Form of Molnupiravir) in Rats for Implication in the Dose Adjustment.

The pharmacokinetic alteration of an antimicrobial medication leading to sub-therapeutic plasma level can aid in the emergence of resistance, a global threat nowadays. In this context, molnupiravir (prodrug of EIDD-1931) is the most efficacious orally against corona virus disease (COVID-19). In addition to drug-drug interaction, the pharmacokinetics of a drug can significantly vary during any disease state, leading to disease-drug interaction. However, no information is available for such a recently approved drug. Therefore, we aimed to explore the oral pharmacokinetics of EIDD-1931 in seven chemically induced disease states individually compared to the normal state using various rat models. Induction of any disease situation was confirmed by the disease specific study(s) prior to pharmacokinetic investigations. Compared to the normal state, substantially lowered plasma exposure (0.47- and 0.63-fold) with notably enhanced clearance (2.00- and 1.56-fold) of EIDD-1931 was observed in rats of ethanol-induced gastric injury and carbon tetrachloride-induced liver injury states. Conversely, paclitaxel-induced neuropathic pain and cisplatin-induced kidney injury states exhibited opposite outcomes on oral exposure (1.43- and 1.50-fold) and clearance (0.69- and 0.65-fold) of EIDD-1931. Although the highest plasma concentration (2.26-fold) markedly augmented in the doxorubicin-induced cardiac injury state, streptozocin-induced diabetes and lipopolysaccharide-induced lung injury state did not substantially influence the pharmacokinetics of EIDD-1931. Exploring the possible phenomenon behind the reduced or boosted plasma exposure of EIDD-1931, results suggest the need for dose adjustment in respective diseased conditions in order to achieve desired efficacy during oral therapy of EIDD-1931.

Direct-acting Antivirals for Hepatitis C and Implication for Community Pharmacy Practice.

The treatment options for hepatitis C have undergone noteworthy advancements since direct-acting antivirals (DAAs) were introduced. The selection of a DAA therapy depends on the patient's genotype, treatment history, concomitant comorbidities, and concurrent medications. Pharmacists have a pivotal role in providing clarification and recommendations in selecting appropriate, individualized DAAs for patients. This commentary aims to (1) provide an overview of DAAs on the market for hepatitis C, and (2) describe the implication for the care of patients with hepatitis C in a community pharmacy. Community pharmacists can establish a workflow to recommend appropriate DAA therapy, identify drug interactions, and improve medication adherence and compliance with lab appointments.

Curricular changes in times of crisis: Lessons learned.

BACKGROUND AND PURPOSE: During the early months of the COVID-19 pandemic, experiential education became challenging as sites began to cancel scheduled rotations, and the University of Florida College of Pharmacy had to cancel the first advanced pharmacy practice experience (APPE) block. This was allowable given the excess number of experiential hours built into the curriculum. EDUCATIONAL ACTIVITY AND SETTING: To meet total program credit hour requirements, a six-credit virtual course was created to mimic an experiential rotation. This course was designed to bridge didactic learning with experiential learning. The course included presentation of patient cases, topic discussions, pharmaceutical calculations, self-care cases, disease state management cases, and career development. FINDINGS: Students provided feedback via a survey containing 23 Likert type questions and four open-ended questions. Most students agreed or strongly agreed that participation in self-care scenarios, small group discussions (calculations and topic discussion), and disease state management cases (preceptor dialogue and verbal defense activities) were valuable learning experiences. The verbal defense portion of the disease management case and the self-care scenarios were the most highly rated learning activities. Peer review activities in the career development assignments were seen as the least beneficial component of the course. SUMMARY: This course allowed students an opportunity to further prepare for APPEs in a unique learning environment. The college was able to identify students requiring additional support during APPEs and provide earlier intervention. Additionally, data supported exploring incorporation of new learning activities into the current curriculum.

A path forward in the development of new aerosol drug delivery devices for pediatrics.

Inhaled medications are widely accepted as being the optimal route for treating pediatric respiratory diseases, a leading cause of hospitalization and death. Despite jet nebulizers being the preferred inhalation device for neonates and infants, current devices face performance issues with most of the drug never reaching the target lung location. Previous work has aimed to improve pulmonary drug deposition, yet nebulizer efficiency remains low. The development of an inhalant therapy that is efficacious and safe for pediatrics depends on a well-designed delivery system and formulation. To accomplish this, the field needs to rethink the current practice of basing pediatric treatments on adult studies. The rapidly evolving pediatric patient (i.e. neonates to eighteen) needs to be considered because they are different from adults with respect to airway anatomy, breathing patterns, and adherence. Previous research approaches to improve deposition efficiency have been limited due to the complexity of combining physics, which drives aerosol transport and deposition, and biology, especially within the area of pediatrics. To address these critical knowledge gaps, we need a better understanding of how patient age and disease state affect deposition of aerosolized drugs. The complexity of the multiscale respiratory system makes scientific investigation very challenging. The authors have simplified the complex problem into five components with these three areas as ones to address first: how the aerosol is (i) generated in a medical device, (ii) delivered to the patient, and (iii) deposited inside the lung. In this review, we discuss the technological advances and innovations made from experiments, simulations, and predictive models in each of these areas. In addition, we discuss the impact on patient treatment efficacy and recommend a clinical direction, with a focus on pediatrics. In each area, a series of research questions are posed and steps for future research to improve efficacy in aerosol drug delivery are outlined.

Factors associated with renal function state transitions: A population-based community survey in Taiwan.

Background: Understanding renal function state transition risk and associated factors in community residences is vital for appropriate preventive and care actions. We aim to investigate factors affecting renal function state transitions through 10-year longitudinal community screening surveys. Methods: The prospective cohort study included participants who attended the screening program ≥2 times from 2001 to 2009 and were divided into two cohorts: those with baseline estimated glomerular filtration rate (eGFR) ≥60 (n = 46,278) and those with eGFR 59-30 mL/min/1.73 m2 (n = 4,656). We applied the illness-death model to identify associated factors with eGFR <60 and death for the cohort with baseline eGFR ≥60 and eGFR <30 and death for that with baseline eGFR ≥59-30. Results: Among the followed-up participants, 3,018 (6.5%) in the cohort of baseline eGFR ≥60 mL/min/1.73 m2 and 322 (6.9%) in the cohort of eGFR 59-30 mL/min/1.73 m2 experienced renal function state transition during a median over 7-year follow-up. Besides eGFR and grade of proteinuria, diabetes mellitus (adding nearly 50% hazard rate) is the main factor associated with both state transitions. Other early-phase eGFR state transition risk factors were metabolic syndrome score, triglyceride, uric acid, fasting blood sugar, and high-density lipoprotein cholesterol. Males, poor hemoglobin, high triglyceride, and high low-density lipoprotein cholesterol were all linked with the late-phase eGFR state transition hazard rate. Conclusion: The study developed the state transition functions for community participants with varying renal function levels. Further actions to develop precision screening plans and services that incorporate personal risk factors and state transition risks are necessary.

A simple pre-disease state prediction method based on variations of gene vector features.

BACKGROUND: The progression of disease can be divided into three states: normal, pre-disease, and disease. Since a pre-disease state is the tipping point of disease deterioration, accurately predicting pre-disease state may help to prevent the progression of disease and develop feasible treatment in time. METHODS: In the perspective of gene regulatory network, the expression of a gene is regulated by its upstream genes, and then it also regulates that of its downstream genes. In this study, we define the expression value of these genes as a gene vector to depict its state in a specific sample. Then, we propose a novel pre-disease prediction method by such vector features. RESULTS: The results of an influenza virus infection dataset show that our method can successfully predict the pre-disease state. Furthermore, the pre-disease state related genes predicted by our methods are highly associated with each other and enriched in influenza virus infection related pathways. In addition, our method is more time efficient in calculation than previous works. The code of our method is accessed at https://github.com/ZhenshenBao/sPGVF.git.

Distinct transcriptome architectures underlying lupus establishment and exacerbation.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease involving multiple immune cells. To elucidate SLE pathogenesis, it is essential to understand the dysregulated gene expression pattern linked to various clinical statuses with a high cellular resolution. Here, we conducted a large-scale transcriptome study with 6,386 RNA sequencing data covering 27 immune cell types from 136 SLE and 89 healthy donors. We profiled two distinct cell-type-specific transcriptomic signatures: disease-state and disease-activity signatures, reflecting disease establishment and exacerbation, respectively. We then identified candidate biological processes unique to each signature. This study suggested the clinical value of disease-activity signatures, which were associated with organ involvement and therapeutic responses. However, disease-activity signatures were less enriched around SLE risk variants than disease-state signatures, suggesting that current genetic studies may not well capture clinically vital biology. Together, we identified comprehensive gene signatures of SLE, which will provide essential foundations for future genomic and genetic studies.

Identifying the critical states and dynamic network biomarkers of cancers based on network entropy.

BACKGROUND: There are sudden deterioration phenomena during the progression of many complex diseases, including most cancers; that is, the biological system may go through a critical transition from one stable state (the normal state) to another (the disease state). It is of great importance to predict this critical transition or the so-called pre-disease state so that patients can receive appropriate and timely medical care. In practice, however, this critical transition is usually difficult to identify due to the high nonlinearity and complexity of biological systems. METHODS: In this study, we employed a model-free computational method, local network entropy (LNE), to identify the critical transition/pre-disease states of complex diseases. From a network perspective, this method effectively explores the key associations among biomolecules and captures their dynamic abnormalities. RESULTS: Based on LNE, the pre-disease states of ten cancers were successfully detected. Two types of new prognostic biomarkers, optimistic LNE (O-LNE) and pessimistic LNE (P-LNE) biomarkers, were identified, enabling identification of the pre-disease state and evaluation of prognosis. In addition, LNE helps to find "dark genes" with nondifferential gene expression but differential LNE values. CONCLUSIONS: The proposed method effectively identified the critical transition states of complex diseases at the single-sample level. Our study not only identified the critical transition states of ten cancers but also provides two types of new prognostic biomarkers, O-LNE and P-LNE biomarkers, for further practical application. The method in this study therefore has great potential in personalized disease diagnosis.

Detect the early-warning signals of diseases based on signaling pathway perturbations on a single sample.

BACKGROUND: During the pathogenesisof complex diseases, a sudden health deterioration will occur as results of the cumulative effect of various internal or external factors. The prediction of an early warning signal (pre-disease state) before such deterioration is very important in clinical practice, especially for a single sample. The single-sample landscape entropy (SLE) was proposed to tackle this issue. However, the PPI used in SLE was lack of definite biological meanings. Besides, the calculation of multiple correlations based on limited reference samples in SLE is time-consuming and suspect. RESULTS: Abnormal signals generally exert their effect through the static definite biological functions in signaling pathways across the development of diseases. Thus, it is a natural way to study the propagation of the early-warning signals based on the signaling pathways in the KEGG database. In this paper, we propose a signaling perturbation method named SSP, to study the early-warning signal in signaling pathways for single dynamic time-series data. Results in three real datasets including the influenza virus infection, lung adenocarcinoma, and acute lung injury show that the proposed SSP outperformed the SLE. Moreover, the early-warning signal can be detected by one important signaling pathway PI3K-Akt. CONCLUSIONS: These results all indicate that the static model in pathways could simplify the detection of the early-warning signals.

Dynamical network biomarkers: Theory and applications.

This paper reviews theory of DNB (Dynamical Network Biomarkers) and its applications including both modern medicine and traditional medicine. We show that omics data such as gene/protein expression profiles can be effectively used to detect pre-disease states before critical transitions from healthy states to disease states by using the DNB theory. The DNB theory with big biological data is expected to lead to ultra-early precision and preventive medicine.

Towards the understanding of state-independent neural traits underlying psychiatric disorders.

Hampered by the symptom complexity and diversity, the understanding of fundamental mechanisms underlying psychiatric disorders remains elusive. Traditional neuroscience research focusing on each behavioral domain separately may lack an overarching view of the pathogenesis of an entire disorder, offering limited power to identify core neuropathology that could possibly account for the disorder's various symptoms. The search for neural traits that are robustly present across different brain functional states and disease stages may provide insights into the rudimentary changes beneath manifest clinical phenotypes and thus help penetrate the causal mechanisms underlying a complex disorder. In this review, I briefly summarize previous research on this topic, emphasize how neural traits may help boost the understanding of biological mechanisms underlying psychiatric disorders, and exemplify how the observed traits may aid individualized predictions for diagnosis and prognosis in precision psychiatry, in particular related to schizophrenia. I also discuss a proposed research framework that can be leveraged for future studies on neural traits, as well as considerations for future applications of this nascent research strategy.

Particulate and drug-induced toxicity assessed in novel quadruple cell human primary hepatic disease models of steatosis and pre-fibrotic NASH.

In an effort to replace, reduce and refine animal experimentation, there is an unmet need to advance current in vitro models that offer features with physiological relevance and enhanced predictivity of in vivo toxicological output. Hepatic toxicology is key following chemical, drug and nanomaterials (NMs) exposure, as the liver is vital in metabolic detoxification of chemicals as well as being a major site of xenobiotic accumulation (i.e., low solubility particulates). With the ever-increasing production of NMs, there is a necessity to evaluate the probability of consequential adverse effects, not only in health but also in clinically asymptomatic liver, as part of risk stratification strategies. In this study, two unique disease initiation and maintenance protocols were developed and utilised to mimic steatosis and pre-fibrotic NASH in scaffold-free 3D liver microtissues (MT) composed of primary human hepatocytes, hepatic stellate cells, Kupffer cells and sinusoidal endothelial cells. The characterized diseased MT were utilized for the toxicological assessment of a panel of xenobiotics. Highlights from the study included: 1. Clear experimental evidence for the pre-existing liver disease is important in the augmentation of xenobiotic-induced hepatotoxicity and 2. NMs are able to activate stellate cells. The data demonstrated that pre-existing disease is vital in the intensification of xenobiotic-induced liver damage. Therefore, it is imperative that all stages of the wide spectrum of liver disease are incorporated in risk assessment strategies. This is of significant consequence, as a substantial number of the general population suffer from sub-clinical liver injury without any apparent or diagnosed manifestations.

DrugSim2DR: systematic prediction of drug functional similarities in the context of specific disease for drug repurposing.

BACKGROUND: Traditional approaches to drug development are costly and involve high risks. The drug repurposing approach can be a valuable alternative to traditional approaches and has therefore received considerable attention in recent years. FINDINGS: Herein, we develop a previously undescribed computational approach, called DrugSim2DR, which uses a network diffusion algorithm to identify candidate anticancer drugs based on a drug functional similarity network. The innovation of the approach lies in the drug-drug functional similarity network constructed in a manner that implicitly links drugs through their common biological functions in the context of a specific disease state, as the similarity relationships based on general states (e.g., network proximity or Jaccard index of drug targets) ignore disease-specific molecular characteristics. The drug functional similarity network may provide a reference for prediction of drug combinations. We describe and validate the DrugSim2DR approach through analysis of data on breast cancer and lung cancer. DrugSim2DR identified some US Food and Drug Administration-approved anticancer drugs, as well as some candidate drugs validated by previous studies in the literature. Moreover, DrugSim2DR showed excellent predictive performance, as evidenced by receiver operating characteristic analysis and multiapproach comparisons in various cancer datasets. CONCLUSIONS: DrugSim2DR could accurately assess drug-drug functional similarity within a specific disease context and may more effectively prioritize disease candidate drugs. To increase the usability of our approach, we have developed an R-based software package, DrugSim2DR, which is freely available on CRAN (https://CRAN.R-project.org/package=DrugSim2DR).

Impact of pharmacist-led interventions on diabetes management at a community pharmacy in Pakistan: A quasi-experimental study.

OBJECTIVE: To determine the impact of pharmacist-led interventions on satisfaction, disease state knowledge and perception of self-management of diabetes patients. METHODS: The interventional, quasi-experimental study was conducted in Bahawalpur, Pakistan, from December 1, 2018, to June 30, 2019, and comprised data data from a community pharmacy of patients who had diagnosed type 2 diabetes for at least one year. Both non-pharmacological and pharmacological interventional tools were used as part of a care package administered by pharmacists. The modified version of the Diabetes Disease State Management Questionnaire was used to assess patient satisfaction, disease state knowledge and perception of self-management. Data was analysed using SPSS 20. RESULTS: Of the 100 patients initially enrolled, 80(80%) completed the follow-up. The mean age of the sample was 49.33±8.31 years. Of the total, 63(78.8%) patients had diabetes for <10 years. A significant improvement was seen in patient satisfaction (p=0.04), disease state knowledge (p=0.009) and self-management of diabetes (p=0.02) scores after the intervention. CONCLUSIONS: Interventions by pharmacists in a community pharmacy resulted in significant improvement in patient satisfaction, disease state knowledge and perception of self-management among type 2 diabetes patients.