Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 28.615
J Pharm Sci ; 112(9): 2313-2320, 2023 09.
Article in English | MEDLINE | ID: mdl-37422281


Though hundreds of drugs have been approved by the US Food and Drug Administration (FDA) for treating various rare diseases, most rare diseases still lack FDA-approved therapeutics. To identify the opportunities for developing therapies for these diseases, the challenges of demonstrating the efficacy and safety of a drug for treating a rare disease are highlighted herein. Quantitative systems pharmacology (QSP) has increasingly been used to inform drug development; our analysis of QSP submissions received by FDA showed that there were 121 submissions as of 2022, for informing rare disease drug development across development phases and therapeutic areas. Examples of published models for inborn errors of metabolism, non-malignant hematological disorders, and hematological malignancies were briefly reviewed to shed light on use of QSP in drug discovery and development for rare diseases. Advances in biomedical research and computational technologies can potentially enable QSP simulation of the natural history of a rare disease in the context of its clinical presentation and genetic heterogeneity. With this function, QSP may be used to conduct in-silico trials to overcome some of the challenges in rare disease drug development. QSP may play an increasingly important role in facilitating development of safe and effective drugs for treating rare diseases with unmet medical needs.

Network Pharmacology , Pharmacology , United States , Humans , Rare Diseases/drug therapy , Models, Biological , Drug Development , Drug Discovery , Pharmaceutical Preparations
CPT Pharmacometrics Syst Pharmacol ; 12(7): 889-903, 2023 Jul.
Article in English | MEDLINE | ID: mdl-37452454


Typical Quantitative Systems Pharmacology (QSP) workflows involve discussion of biology, supported by graphical diagrams, followed by construction of large Ordinary Differential Equation models. QSP Designer facilitates this process by providing enhanced graphical notation, which enables hierarchical presentation with modules and handling of combinatorial complexity with diagram node arrays. Whereas the software includes a simulation engine, a major feature is full model code generation in MATLAB, R, C, and Julia to support multiple modeling communities.

Network Pharmacology , Pharmacology , Humans , Models, Biological , Software , Computer Simulation , Language
Br J Pharmacol ; 180 Suppl 1: 3-1425, 2023 07.
Article in English | MEDLINE | ID: mdl-37366174
BMC Med Educ ; 23(1): 471, 2023 Jun 23.
Article in English | MEDLINE | ID: mdl-37353802


BACKGROUND: Knowledge of pharmacology is crucial for physicians to perform rational and safe medicine. Medical professionals are responsible for prescribing drugs and a weak performace of those can result in medication errors leading to disability, hospitalization, and death, among other situations. It occurs worldwide, including in Brazil, so that learning pharmacology impacts on public health service. We aim to investigate the current pharmacology educational practices in medical schools in the state of Rio de Janeiro, Brazil. METHODS: We surveyed 14 of 22 medical schools in Rio de Janeiro. Pharmacology teachers (n=16) and medical students (n=89) answered a semi-structured questionnaire that included questions about the staff characteristics, pharmacology content, teacher's concepts, and common practices and resources that were used in pharmacology classes. RESULTS: Our results revealed that the medical schools had similar overall curriculums. Pharmacology teachers work more than 30hs a week (75%) and conducted both research and teaching (62.5%). We also found that the multimedia projector was the most common resource (71.9%), and passive pedagogical methodologies (e.g., expository classes) remain a current strategy in pharmacology classes (89.9%). In general, medical students are poorly motivated (55%), which may be related to their performance in assessments. In addition, students believe that pharmacology is a complex (52%) or very complex subject (46%) since for its full understanding the student needs concepts from other disciplines, which can have an impact on the performance and motivation of students. As a result, these medical students do not fully understand the integration between pharmacology's basic concepts and their clinical applications. CONCLUSION: These data seem to demonstrate that the adopted teaching and learning pharmacology strategies and methodologies can be improved in Rio de Janeiro.

COVID-19 , Pharmacology , Students, Medical , Humans , Brazil , Pandemics , Learning , Teaching , Pharmacology/education
Metas enferm ; 26(4): 13-20, May. 2023. tab, ilus, graf
Article in Spanish | IBECS | ID: ibc-220030


Objetivo: evaluar el perfil farmacoterapéutico del paciente con ileostomía y validar el uso de una aplicación móvil en las plataformas iOS y Android de carácter universal, gratuito y funcional dirigida a la comunidad de enfermeras expertas en ostomía y probar esta aplicación tanto por médicos como por enfermeras.Método: veinte especialistas en ostomía (10 médicos y 10 enfermeras) respondieron de forma anónima a un cuestionario de prescripción. Se preguntaron un total de 576 ítems en dos fases. La primera fase se llevó a cabo utilizando fuentes tradicionales de consulta y la segunda empleando la app Ostomecum. Se comparó la velocidad (proporción de preguntas respondidas en el tiempo dado) y la precisión (proporción de respuestas correctas) a la hora de contestar las preguntas. También se investigaron diferencias en la evaluación entre diferentes escenarios de práctica de la Enfermería. La comparación se realizó mediante la prueba pareada de Wilcoxon.Resultados: el porcentaje de preguntas respondidas pasó de 36% en la primera ronda con fuentes tradicionales (43% médicos, 30% enfermeras) a 99% en la segunda ronda usando el software de la aplicación (99% médicos, 99% enfermeras). La precisión de las respuestas aumentó del 22% (26% médicos, 18% enfermeras) al 94% (93% médicos, 95% enfermeras). Las diferencias entre médicos y enfermeras en la primera ronda(p< ,0001) se igualaron al emplear Ostomecum (p= ,7882). El nivel de certeza con la respuesta también cambió del 12% (20% médicos, 4% enfermeras) al 60% con la aplicación móvil (70% médicos, 50% enfermeras) (p< ,0001 total y ambos grupos). En cuanto al entorno laboral, la proporción de aciertos aumentó por igual tanto en el ámbito de Enfermería de consultorio como hospitalario (p< 0,0001 en cada uno).Conclusión: el estomaterapeuta con frecuencia es consultado en los cuidados del paciente ostomizado...(AU)

Objectives: to assess the pharmacotherapeutic profile of the patient with ileostomy and to validate the use of a universal, free and functional mobile application in the IOS and Android platforms, targeted to the community of ostomy-expert nurses, and to try this application both by doctors and by nurses.Method: twenty ostomy specialists (10 doctors and 10 nurses) answered anonymously a prescription questionnaire. In total, 576 items were asked in two stages. The first stage was conducted using traditional sources of reference, and the second one using the OSTOMECUM application. There was a comparison in speed (proportion of questions answered within the given time) and accuracy (proportion of correct answers) at the time of answering the questions. There was also research into the differences in assessment among different settings of Nursing practice. The comparison was conducted through the paired samples Wilcoxon test.Results: the proportion of answered questions went from 36% in the first round using traditional sources (43% for doctors, 30% for nurses) to 99% in the second round using the application software (99% for doctors, 99% for nurses). The accuracy in answers increased from 22% (16% for doctors, 18% for nurses) to 94% (93% for doctors, 95% for nurses). Differences between doctors and nurses in the first round(p< .0001) were equalled when using OSTOMECUM (p= .7882). The level of certainty with the answer also changed from 12% (20% for doctors, 4% for nurses) to 60% with the mobile application (70% for doctors, 50% for nurses) (p< .0001 total and both groups). In terms of work setting, the proportion of right answers increased equally both in the outpatient and the hospital Nursing settings (p< 0.0001 in each one).Conclusion: the stomal therapist is frequently consulted about care for ostomized patients. The OSTOMECUM application is a useful tool for reviewing medication and reconsidering the adequacy of a treatment prescribed to ostomized...(AU)

Humans , Technology , Software , Pharmacology , Ostomy , Surveys and Questionnaires , Health Personnel
Brief Bioinform ; 24(3)2023 05 19.
Article in English | MEDLINE | ID: mdl-37031957


Network pharmacology is an emerging area of systematic drug research that attempts to understand drug actions and interactions with multiple targets. Network pharmacology has changed the paradigm from 'one-target one-drug' to highly potent 'multi-target drug'. Despite that, this synergistic approach is currently facing many challenges particularly mining effective information such as drug targets, mechanism of action, and drug and organism interaction from massive, heterogeneous data. To overcome bottlenecks in multi-target drug discovery, computational algorithms are highly welcomed by scientific community. Machine learning (ML) and especially its subfield deep learning (DL) have seen impressive advances. Techniques developed within these fields are now able to analyze and learn from huge amounts of data in disparate formats. In terms of network pharmacology, ML can improve discovery and decision making from big data. Opportunities to apply ML occur in all stages of network pharmacology research. Examples include screening of biologically active small molecules, target identification, metabolic pathways identification, protein-protein interaction network analysis, hub gene analysis and finding binding affinity between compounds and target proteins. This review summarizes the premier algorithmic concepts of ML in network pharmacology and forecasts future opportunities, potential applications as well as several remaining challenges of implementing ML in network pharmacology. To our knowledge, this study provides the first comprehensive assessment of ML approaches in network pharmacology, and we hope that it encourages additional efforts toward the development and acceptance of network pharmacology in the pharmaceutical industry.

Network Pharmacology , Pharmacology , Drug Discovery/methods , Machine Learning , Proteins , Algorithms , Pharmacology/methods
Actas esp. psiquiatr ; 51(2): 46-55, Marzo - Abril 2023. tab
Article in Spanish | IBECS | ID: ibc-220873


Introducción. El trastorno adaptativo es una entidad clínica frecuente pero muy escasamente estudiada en población anciana hospitalizada por causas somáticas. A pesar de su doble consideración como entidad benigna y no subsidiaria de mejoría mediante tratamiento farmacológico, su evolución puede ser tórpida y el empleo de psicofármacos está muy extendido. En una población anciana con pluripatología y polifarmacia, el uso de fármacos podría ser nocivo. Métodos. Estudio descriptivo retrospectivo de 123 pacientes con diagnóstico de trastorno adaptativo atendidos por la Interconsulta de Psicogeriatría de un hospital de tercer nivelen los años 2016 y 2017. Se valoran antecedentes, manejo al diagnóstico y durante el seguimiento posterior hasta el año. Resultados. Un 75,9% del total de pacientes recibieron tratamiento farmacológico al diagnóstico de trastorno adaptativo, mientras que solo un 22,8% son derivados a Psicología clínica. Al alta, solo 50% de los pacientes son derivados a Salud Mental. El 13,8% de los pacientes fueron exitus antes del alta de hospitalización. El 72,6% precisaron durante el año de seguimiento un nuevo reingreso hospitalario, y de este grupo, el 16,6% precisó escalada en la dosis de psicofármacos. Conclusiones. Además de aportar datos sobre el manejo clínico de este perfil complejo y frecuente de pacientes, este trabajo sirve como punto de partida para futuras líneas de investigación que puedan aportar luz sobre un aspecto muy pobremente reflejado en la bibliografía médica actual a pesar del envejecimiento inexorable de la población. (AU)

Introduction. Adaptive disorder is a frequent diagnosisbut poorly studied in the elderly population hospitalized. Despite it is considerate benign and non-subsidiary entityof improvement through pharmacological treatment. It canevolve in a difficult way and the pharmacological treatmentis widespread. The use of drugs could be harmful the elderly population with pluripathology and polypharmacy. Methods. A retrospective descriptive study, total of 123patients diagnosed with adaptive disorder and attended bythe Psychogeriatric Liaison in a third-level hospital between2016 and 2017. Medical history, management at diagnosis andfollow-up until one year after discharge were collected on. Results. At the diagnosis of adaptative disorder the75.9% of all patients received pharmacological treatment, while only 22.8% were referred to psychology. Only 50% ofpatients were referred to mental health upon discharge. The13.8% of patients died before discharge from hospital. During the follow-up year, the 72.6% required a new hospital admission. And of this group, the 16.6% required increasingthe dose of drugs. Conclusions. This study provides data on the clinical management of this complicated and frequent profile ofpatients. In addition, this work is a starting point for future lines of research that can shed light on an aspect very poorly reflected in the current medical literature despite the aging of the population. (AU)

Humans , Aged , Aged, 80 and over , Geriatrics , Adjustment Disorders/psychology , Adjustment Disorders/therapy , Geriatric Psychiatry , Pharmacology , Drug Therapy
Clin Pharmacol Ther ; 113(5): 945-947, 2023 05.
Article in English | MEDLINE | ID: mdl-37069736
CPT Pharmacometrics Syst Pharmacol ; 12(4): 432-443, 2023 04.
Article in English | MEDLINE | ID: mdl-36866520


Quantitative systems pharmacology (QSP) models integrate comprehensive qualitative and quantitative knowledge about pharmacologically relevant processes. We previously proposed a first approach to leverage the knowledge in QSP models to derive simpler, mechanism-based pharmacodynamic (PD) models. Their complexity, however, is typically still too large to be used in the population analysis of clinical data. Here, we extend the approach beyond state reduction to also include the simplification of reaction rates, elimination of reactions, and analytic solutions. We additionally ensure that the reduced model maintains a prespecified approximation quality not only for a reference individual but also for a diverse virtual population. We illustrate the extended approach for the warfarin effect on blood coagulation. Using the model-reduction approach, we derive a novel small-scale warfarin/international normalized ratio model and demonstrate its suitability for biomarker identification. Due to the systematic nature of the approach in comparison with empirical model building, the proposed model-reduction algorithm provides an improved rationale to build PD models also from QSP models in other applications.

Pharmacology , Warfarin , Humans , Warfarin/pharmacology , Network Pharmacology , Models, Biological , Blood Coagulation , Algorithms
An. R. Acad. Nac. Farm. (Internet) ; 89(1): 23-51, Enero-Marzo 2023. graf
Article in English | IBECS | ID: ibc-219534


Apart from their well-stablished antihypercholesterolemic effect, HMG-CoA reductase inhibitors, commonly known as statins, have been described to exert pleotropic effects at different levels, including anti-inflammatory and pro-apoptotic responses. Since its discovery, and based on these properties, a broad range of studies have tried to evaluate their potential beneficial effects in other pathological situations beyond cardiovascular diseases (CVDs). Although statins effects have been evaluated in different types of diseases including not only in vitro and in vivo experiments, but also statin administration in patients, the current bibliography about statins is mainly focused on specific diseases and/or cell types. Hence, in this review, we aim to summarize every virtue attributed to statins in many pathologies, comprehending from the wellknown effects in CVDs to the recent discovered beneficial effects in the COVID-19 disease, trough cancer, brain and autoimmune diseases or even pathogen infections. We include the suggested mechanisms implicated in these effects, the current situation of the use of statins in different pathologies as well as their negative and/or opposite effects stated by some authors. Considering the substantial cost and slow pace of new drugs discovery and development besides the high attrition rates, several authors have remarked the need of repurposing old drugs to treat common and rare diseases. Given the low risk, the low overall development costs and the short development timelines, the purpose of this review is to emphasize the potential use of statins as multitarget drug to treat different pathologies. (AU)

Aparte de la actividad antihipercolesterolémica ampliamente descrita de los inhibidores de la HMG-CoA reductasa, conocidos como estatinas, estos fármacos también ejercen otros efectos pleiotrópicos, incluyendo respuestas antinflamatorias y proapoptóticas. Desde su descubrimiento, numerosos estudios han evaluado los efectos beneficiosos que ejercen en otras patologías diferentes a las que comúnmente se tratan con estatinas, como las enfermedades cardiovasculares (ECVs). Aunque se han evaluado sus efectos en estudiosin vitro e in vivo, así como en pacientes, la bibliografía existente está enfocada al uso de estatinas en una enfermedad o tipo celular concreto, por lo que, en esta revisión, pretendemos resumir en un mismo trabajo todas las virtudes atribuidas a las estatinas en numerosas patologías, que abarcan desde las ECVs hasta los beneficios recientemente descritos en relación a la COVID-19, considerando otras enfermedades comoel cáncer, patologías cerebrales y autoinmunes e incluso infecciones por agentes patógenos. Incluimos los mecanismos descritos en los efectos beneficiosos de las estatinas, la situación actual de su uso en diferentespatologías, así como la descripción de los efectos opuestos o negativos observados por algunos autores. El elevado coste y tiempo que implican el descubrimiento y desarrollo de nuevos fármacos, conlleva quemuchos autores propongan la reutilización de antiguos fármacos para el tratamiento de enfermedades tanto comunes como raras. Considerando el bajo riesgo, los bajos costes relativos de producción y los cortosplazos de desarrollo, el propósito de esta revisión es focalizar el potencial uso de las estatinas como fármacosmultiusos para el tratamiento de diferentes enfermedades. (AU)

Humans , Hydroxymethylglutaryl-CoA Reductase Inhibitors/therapeutic use , Cardiovascular Diseases/drug therapy , Cardiovascular Diseases/immunology , Communicable Diseases/drug therapy , Pharmacology , Kidney Neoplasms/drug therapy , Kidney Neoplasms/therapy , Autoimmune Diseases/drug therapy
Nurse Educ Today ; 124: 105756, 2023 May.
Article in English | MEDLINE | ID: mdl-36821948


BACKGROUND: Nursing students require learning strategies when studying pharmacology. The COVID-19 pandemic has increased the prevalence of online self-study. The design of effective online learning materials has therefore become vital to nursing education. OBJECTIVES: The objective of this study was to describe the active learning mechanism that helped nursing students learn pharmacology through interactive learning materials and to demonstrate that no increased cognitive load in nursing students when studying pharmacology using interactive learning materials. METHOD: We designed an active learning mechanism to help nursing students study pharmacology by using interactive learning materials. An experimental pre- and post-test design was conducted. The participants were second-year nursing students (age 16-17) in a junior college of nursing. Students were randomly assigned to an experimental group (n = 98) and a control group (n = 90). RESULTS: We developed multi-media interactive learning materials and an active learning mechanism to enable nursing students to learn pharmacology. The proposed approach not only improved learning achievements but also reduced the cognitive load of nursing students. CONCLUSION: The major contribution of this study exhibits a new approach to practice wherein active learning is incorporated into interactive pharmacology materials for nursing students. This can be attributed to the design features of "explanation," "quiz and feedback," and "encouragement." Our results aid the development of effective interactive learning materials for pharmacology for Taiwanese nursing students.

COVID-19 , Education, Nursing, Baccalaureate , Pharmacology , Students, Nursing , Humans , Adolescent , Problem-Based Learning/methods , Students, Nursing/psychology , Pandemics , Cognition , Teaching , Pharmacology/education