Developing Health Literacy Knowledge and Skills Through Case-Based Learning

Objective. To evaluate the efficacy of case-based learning to teach pharmacy students health literacy concepts and skills in managing patients with limited health literacy.Design. A health literacy patient case was developed and incorporated into a case-based learning laboratory. The case involved a patient with limited health literacy and required students to evaluate and formulate a care plan.Assessment. A comparison of pretest and posttest scores demonstrated that students gained health literacy knowledge and skills through completion of the patient case. Students believed that the case-based exercise was successful in meeting specific learning objectives for the course.Conclusions. Addition of a case-based learning was effective in teaching pharmacy students health literacy concepts and skills.     

X线监视下食管异物的诊断和治疗

目的:探讨食管异物的X诊断和低张钡棉加温水冲服治疗技术.方法:对73例食管异物患者常规检查前肌注15 mg 654-2或者阿托品1.5 mg,10 min后在GE-TH600 X线机电视监视下,胸腹部透视和(或)点片,如发现不透X线异物(如金属异物)平片多数能明确诊断,少数加服钡剂证实;较大透X线异物采用口服钡剂,较小透X线异物采用口服医用纯净BaSO4和少量脱脂棉混悬液检查,确定异物后,反复温水冲服直至异物去除引入胃腔内.结果:统计经X线检查为食管异物阳性51例,阴性22例;不透X线异物11例,透光异物62例;X线透视吞钡棉去除异物70例,经纤维内镜处理异物3例.结论:GE-TH600电视X线机电视监视下低张钡棉加温水冲服治疗技术去除食管异物,诊断和疗效满意,值得临床推广.     

我国血液制品企业GMP检查缺陷分析及监管思路探索

目的：为完善我国药品GMP管理,提高企业GMP管理水平提供参考。方法：基于检查37家（次）血液制品生产企业在实施GMP中存在的381项缺陷,采用数据统计的方法进行分类评估,并提出针对性建议。结果与结论：目前,在我国血液制品GMP管理中,存在部分企业实施GMP主动意识不强、企业违反GMP成本较低等问题。建议通过落实企业主体责任、加大检查及处罚力度等办法,减少目前我国血液制品GMP监管中的问题。     

Predicting Methylphenidate Response in ADHD Using Machine Learning Approaches

Background:

There are no objective, biological markers that can robustly predict methylphenidate response in attention deficit hyperactivity disorder. This study aimed to examine whether applying machine learning approaches to pretreatment demographic, clinical questionnaire, environmental, neuropsychological, neuroimaging, and genetic information can predict therapeutic response following methylphenidate administration.

Methods:

The present study included 83 attention deficit hyperactivity disorder youth. At baseline, parents completed the ADHD Rating Scale-IV and Disruptive Behavior Disorder rating scale, and participants undertook the continuous performance test, Stroop color word test, and resting-state functional MRI scans. The dopamine transporter gene, dopamine D4 receptor gene, alpha-2A adrenergic receptor gene (ADRA2A) and norepinephrine transporter gene polymorphisms, and blood lead and urine cotinine levels were also measured. The participants were enrolled in an 8-week, open-label trial of methylphenidate. Four different machine learning algorithms were used for data analysis.

Results:

Support vector machine classification accuracy was 84.6% (area under receiver operating characteristic curve 0.84) for predicting methylphenidate response. The age, weight, ADRA2A MspI and DraI polymorphisms, lead level, Stroop color word test performance, and oppositional symptoms of Disruptive Behavior Disorder rating scale were identified as the most differentiating subset of features.

Conclusions:

Our results provide preliminary support to the translational development of support vector machine as an informative method that can assist in predicting treatment response in attention deficit hyperactivity disorder, though further work is required to provide enhanced levels of classification performance.     

Enabling Tablet Product Development of 5-Fluorocytosine Through Integrated Crystal and Particle Engineering

The antifungal drug, 5-fluorocytosine (FC), is marketed as a capsule (250 or 500 mg strength) instead of the preferred tablet dosage form. Through systematic characterization of solid-state properties, including mechanical properties, we identify tabletability and poor physical stability of FC as the problems that likely have prevented the successful development of a FC tablet product. We then design an FC oxalate 2:1 salt (FCOXA21), based on established relationship between crystal structure and properties, to address these deficient properties. FCOXA21 is subsequently used to develop a direct compression tablet product using predictive and material-sparing powder characterization tools, that is, ring shear cell for powder flowability and compaction simulator for powder tabletability. The initial tablet formulation, which contains 84.5% (wt %) FCOXA21, exhibits excellent tabletability but inadequate flowability. We solve the powder flowability problem through controlling the particle size of FCOXA21. A batch of FCOXA21 tablets (500 mg FC equivalent dose) is then prepared. Finally, systematic evaluation on tablet weight variation, content uniformity, friability, and dissolution using standard methods confirms the commercial manufacturability of FC tablets. Through this work, we have demonstrated the potential of integrated crystal and particle engineering in expediting the development of tablet products of challenging drugs using the economical direct compression process. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 103:1126–1132, 2014     

Applications of Machine Learning in Solid Oral Dosage Form Development

This review comprehensively summarizes the application of machine learning in solid oral dosage form development over the past three decades. In both academia and industry, machine learning is increasingly applied for multiple preformulation/formulation and process development studies. Further, this review provides the authors’ perspectives on how pharmaceutical scientists can use machine learning for right projects and in right ways; some key ingredients include (1) the determination of inputs, outputs, and objectives; (2) the generation of a database containing high-quality data; (3) the development of machine learning models based on dataset training and model optimization; (4) the application of trained models in making predictions for new samples. It is expected by the authors and others that machine learning will promisingly play a more important role in tomorrow's projects for solid oral dosage form development.     

引用“问题道向式学习”实践医学大学“生命伦理教学之理念”

医学教育的目的不仅只在于生命科学知识（to know；knowledge）的汲取及医事技术（todo；skins）的培训？更重要的是专业素质与人口（to be；professionalism）的培养。传统医学教育过份强调生命科学知识内容的汲取与医疗技术熟练的应用，而忽略了对人性尊严的专注及职业道德的培训。黄昆严教授在医学教育改革的白皮书中说“学医要先学会做人”。因此行医就要以人为本。人是个有灵性的生命体，人有伦而事有理；待人接物之道艮即为伦理。“生命伦理”一词虽在1969年才首度出现，然而随着现代科技急遽发展，西方社会热切讨论，目前已成为相当完备的一门显学。在医学上，50年代制造出的呼吸器与60年代研发出的洗肾、移植技术，以致于近来遗传基因研究的突破，都让伦理的讨论超越了以往传统医病关系的范畴。而其所肩负“平衡科技带来之风险与利益”，不但是人类社会将面临之挑战，更是医学专业无可逃避的责任。为了全面推行生命伦理教育于医学大学，研拟一套有效可行的学习方法应属当务之急。生命伦理之教学目标，除了培养思辨与做决定的能力外，还希望能成就医学生道德与价值信念之内化过程。而传统单科大班授课，在培养学生主动学习的态度上及整合知识应用于生活情境中皆有所限制，尤其是在营造推理、价值反思的互动学习上更是难有成就。     

Creation of Medicinal Chemistry Learning Communities Through Enhanced Technology and Interdisciplinary Collaboration

Objectives. To build an integrated medicinal chemistry learning community of campus and distance pharmacy students though the use of innovative technology and interdisciplinary teaching.Design. Mechanisms were implemented to bring distance students into campus-based medicinal chemistry classrooms in real time, stimulate interaction between instructors and various student cohorts, and promote group work during class. Also, pharmacy clinician colleagues were recruited to contribute to the teaching of the 3 medicinal chemistry courses.Assessment. Student perceptions on the value of technology to build community and advance learning were gleaned from course evaluations, in class feedback, and conversations with class officers and student groups. Responses on a survey of second-year students confirmed the benefits of interdisciplinary content integration on engagement and awareness of the connection between drug chemistry and pharmacy practice. A survey of clinician colleagues who contributed to teaching the 3 medicinal chemistry courses found their views were similar to those of students.Conclusions. The purposeful use of technology united learners, fostered communication, and advanced content comprehension in 3 medicinal chemistry courses taught to campus and distance students. Teaching collaboration with pharmacy clinicians enhanced learner interest in course content and provided insight into the integrated nature of the profession of pharmacy.     

Optimization of Vancomycin Initial Dose in Term and Preterm Neonates by Machine Learning

Pharmaceutical Research - Vancomycin is one of the antibiotics most used in neonates. Continuous infusion has many advantages over intermittent infusions, but no consensus has been achieved...     

机器学习在抗体制剂不溶性微粒分类识别中的应用

目的：对微流成像技术收集的抗体注射剂的不溶性微粒图片进行分类建模,以建立不溶性微粒的分类和溯源分析方法。方法：本研究首先制备并用微流成像系统获得气泡、硅油液滴、玻璃颗粒、反复冻融产生的蛋白颗粒4种不同类型颗粒的图片,并将这些图片分为训练集和测试集两部分。采用3种卷积神经网络（Convolutional Neural Network,CNN）模型,即ResNet50、DenseNet201和ShuffleNetV2,对训练集中的图片进行训练和识别,建立数据集,并用于对测试集图片的识别。此外,通过在实际样品中的应用,将CNN模型识别和人眼分类的准确性和速度进行对比。结果与结论：各模型对于测试集的识别准确率都达到96%以上,且DenseNet201模型具有更优的稳定性;与人眼识别相比,准确率没有显著差异,识别速度更快且具有显著差异。本研究表明CNN模型能够对蛋白质制剂中不溶性微粒图片进行分类和溯源分析,以便采取有针对性的措施,降低药品的潜在风险和安全隐患。     

Heroin in Malaysia and Singapore

Clandestine heroin laboratories have been a feature of the Malaysian illicit drug scene since soon after the abuse of heroin emerged in 1972. The first few clandestine heroin laboratories which synthesised heroin via the acetylation of imported morphine were uncovered in 1973 and 1977. By the mid‐1980s, this type of laboratory was replaced by heroin‐cutting laboratories whereby imported high‐grade heroin was cut to street heroin. This was to meet the rising demand for the drug owing to the rapid escalation of the number of drug users. Over the years, the most significant change in the composition of the street heroin is the decrease in its purity from 30%–50% to 3%–5%. Caffeine has remained the major adulterant and chloroquine is detected in virtually all recent seizures.     

典型机器学习算法在脂肪肝分类预测研究中的实现与比较

目的:实现和比较5种常见的机器学习算法在脂肪肝分类预测研究中的应用。方法:通过主成分分析对数据的体检指标进行降维,然后应用决策树、神经网络、支持向量机、贝叶斯网络和随机森林算法,分别构建脂肪肝分类预测模型,对1956例体检数据进行脂肪肝的分类预测。结果:决策树分类模型在脂肪肝分类预测上的准确率最高,达到70.14%,其次是支持向量机和神经网络模型,处于68%左右的水平。结论:本文所研究的典型算法在脂肪肝分类的预测上具有较为可靠分类预测能力,但决策树模型在应用小样本数据上表现出了优势;同时,还发现臀围(HIP)和甘油三酯(TG)可能与脂肪肝分类关联密切。     

分子动力学模拟和机器学习结合在药物设计领域的应用

近年来,传统计算机辅助药物分子设计(computer-aided drug molecular design,CADD)和人工智能技术的融合为创新药物发现带来新的契机。分子动力学模拟(molecular dynamics simulation,MD)是CADD中一项不可或缺的经典技术,作为一种强大的模拟计算方法,常常用来研究靶标和药物之间相互作用的动态过程;机器学习(machine learning,ML)作为一种数据驱动的建模方法,正在被广泛应用于药物发现的各个阶段。MD和ML内在互补的特性,为两者的有机结合提供了诸多可能。一方面,ML可以用来分析MD中产生的海量、高维结构动态信息,通过特征提取、降维等策略,识别关键状态和构象,阐明生物体系动态演变背后的潜在机制;另一方面,MD产生的包含结构动态信息的数据可以用于ML模型训练,提高模型对靶标-药物体系热力学性质和动力学性质的预测精度。因此,MD和ML的有效结合在基于结构的药物设计领域的应用具有重要意义。     

Prediction Machines: Applied Machine Learning for Therapeutic Protein Design and Development

The rapid growth in technological advances and quantity of scientific data over the past decade has led to several challenges including data storage and analysis. Accurate models of complex datasets were previously difficult to develop and interpret. However, improvements in machine learning algorithms have since enabled unparalleled classification and prediction capabilities. The application of machine learning can be seen throughout diverse industries due to their ease of use and interpretability. In this review, we describe popular machine learning algorithms and highlight their application in pharmaceutical protein development. Machine learning models have now been applied to better understand the nonlinear concentration dependent viscosity of protein solutions, predict protein oxidation and deamidation rates, classify sub-visible particles and compare the physical stability of proteins. We also applied several machine learning algorithms using previously published data and describe models with improved predictions and classification. The authors hope that this review can be used as a resource to others and encourage continued application of machine learning algorithms to problems in pharmaceutical protein development.     

Environmental concerns and diet in Singapore

Many factors affect how people perceive the world and their environment, and how such perceptions affect lifestyle decisions, yet the relationship between personal perceptions about environmental hazards and diet is rarely examined. In this study, environmental concerns on a local and global scale were examined, along with dietary patterns, to determine if there were associations and age-related differences. The hypothesis that concerns about water pollution might be reflected in choices about seafood consumption was examined. Many aspects of dietary choices varied significantly by age, including (1) younger people ate more "fast food" than others, (2) subjects aged 22-32 yr ate more meals in restaurants than older or younger people, (3) older individuals drank more tea than younger subjects, while younger people drank more soda than older people, and (4) there were few significant differences in total fish and shellfish meals, although young people ate more meals of other types of meat. Individuals who rated their health the best reported significantly fewer seafood meals than others. People who listed pollution as the major environmental problem did not consume seafood less often than others. While pollution was listed as the most important environmental problem globally, people distinguished between air and water pollution only for Singapore. Although Singapore, with its rapidly expanding economy, has reason to be concerned about both water and air pollution, only 14% of those listing pollution as the main Singapore environmental problem mentioned "water pollution." Singapore has been able to take measures to reduce water pollution from internal sources. Respondents considered air pollution a greater problem, perhaps reflecting recent haze disasters from deliberately set forest fires in Indonesia, which are beyond the direct control of Singapore or Singaporeans.     

Paracetamol elimination in Chinese and Indians in Singapore

Summary The 24 h urinary excretion of paracetamol and its conjugates after oral administration of about 15 mg/kg was studied in 24 Chinese and 24 Indian healthy young adult male volunteers living in Singapore.The Indians excreted a significantly lower fraction of sulphate conjugate (28.9% compared to 35.9% in the Chinese), and a correspondingly higher fraction as glucuronide conjugate (62.2% compared to 54.5% in the Chinese). There was no difference between the ethnic groups in terms of the urinary recovery of unchanged paracetamol (Indians 3.4%, Chinese 3.6%), glutathione-derived cysteine (Indians 2.3% and Chinese 2.2%) and mercapturic acid (Indians 3.6%, Chinese 3.8%) conjugates. The total fraction of administered paracetamol recovered in the urine was 84.7% and 85.7% in the Indian and the Chinese groups, respectively. Although the total glutathione derived conjugates recovered in the two ethnic groups were similar (Indians 5.9%, Chinese 6%), the values were lower than those reported previously for Caucasians in Scotland (9.3%).     

Machine Learning and Accelerated Stress Approaches to Differentiate Potential Causes of Aggregation in Polyclonal Antibody Formulations During Shipping

Therapeutic proteins are among the most widely prescribed medications, with wide distribution and complex supply chains. Shipping exposes protein formulations to stresses that can trigger aggregation, although the exact mechanism(s) responsible for aggregation are unknown. To better understand how shipping causes aggregation, we compared populations of aggregates that were formed in a polyclonal antibody formulation during live shipping studies to populations observed in accelerated stability studies designed to mimic both the sporadic high g-force and continuous low g-force stresses encountered during shipping. Additionally, we compared the effects on aggregation levels generated in two types of secondary packaging, one of which was designed to mitigate the effects of large g-force stresses. Aggregation was quantified using fluorescence intensity of 4,4′-dianilino-1,1′-binaphthyl-5,5′-disulfonic acid (bis-ANS) dye, size exclusion high performance liquid chromatography (SECHPLC), and flow imaging microscopy (FIM). FIM was also combined with machine learning methods to analyze particle morphology distributions. These comparisons revealed that the morphology distributions of aggregates formed during live shipping resemble distributions that result from low g-force events, but not those observed following high g-force events, suggesting that low g-force stresses play a predominant role in shipping-induced aggregation.     

Summary Workshop Report: Facilitating Oral Product Development and Reducing Regulatory Burden Through Novel Approaches to Assess Bioavailability/Bioequivalence

This summary workshop report highlights presentations and over-arching themes from an October 2011 workshop. Discussions focused on best practices in the application of biopharmaceutics in oral drug product development and evolving bioequivalence approaches. Best practices leverage biopharmaceutic data and other drug, formulation, and patient/disease data to identify drug development challenges in yielding a successfully performing product. Quality by design and product developability paradigms were discussed. Development tools include early development strategies to identify critical absorption factors and oral absorption modeling. An ongoing theme was the desire to comprehensively and systematically assess risk of product failure via the quality target product profile and root cause and risk analysis. However, a parallel need is reduced timelines and fewer resources. Several presentations discussed applying Biopharmaceutics Classification System (BCS) and in vitro-in vivo correlations in development and in post-development and discussed both resource savings and best scientific practices. The workshop also focused on evolving bioequivalence approaches, with emphasis on highly variable products (HVDP), as well as specialized modified-release products. In USA, two bioequivalence approaches for HVDP are the reference-scaled average bioequivalence approach and the two-stage group-sequential design. An adaptive sequential design approach is also acceptable in Canada. In European Union, two approaches for HVDP are a two-stage design and an approach to widen C (max) acceptance limits. For some specialized modified-release products, FDA now requests partial area under the curve. Rationale and limitations of such metrics were discussed (e.g., zolpidem and methylphenidate). A common theme was the benefit of the scientific and regulatory community developing, validating, and harmonizing newer bioequivalence methodologies (e.g., BCS-based waivers and HVDP trial designs).