Development of bispecific T cell engagers: harnessing quantitative systems pharmacology.

Bispecific T cell engagers (bsTCEs) have emerged as a promising class of cancer immunotherapy. Several bsTCEs have achieved marketing approval; dozens more are under clinical investigation. However, the clinical development of bsTCEs remains rife with challenges, including nuanced pharmacology, limited translatability of preclinical findings, frequent on-target toxicity, and convoluted dosing regimens. In this opinion article we present a distinct perspective on how quantitative systems pharmacology (QSP) can serve as a powerful tool for overcoming these obstacles. Recent advances in QSP modeling have empowered developers of bsTCEs to gain a deeper understanding of their context-dependent pharmacology, bridge gaps in experimental data, guide first-in-human (FIH) dose selection, design dosing regimens with expanded therapeutic windows, and improve long-term treatment outcomes. We use recent case studies to exemplify the potential of QSP techniques to support future bsTCE development.

Safety pharmacology 2023 and implementation of the ICH E14/S7B Q&A guidance document.

This editorial prefaces the annual themed issue on safety pharmacology (SP) methods published since 2004 in the Journal of Pharmacological and Toxicological Methods (JPTM). We highlight here the content derived from the recent 2022 Safety Pharmacology Society (SPS) and Canadian Society of Pharmacology and Therapeutics (CSPT) joint meeting held in Montreal, Quebec, Canada. The meeting also generated 179 abstracts (reproduced in the current volume of JPTM). As in previous years the manuscripts reflect various areas of innovation in SP including a comparison of the sensitivity of cross-over and parallel study designs for QTc assessment, use of human-induced pluripotent stem cell (hi-PSC) neuronal cell preparations for use in neuropharmacological safety screening, and hiPSC derived cardiac myocytes in assessing inotropic adversity. With respect to the latter, we anticipate the emergence of a large data set of positive and negative controls that will test whether the imperative to miniaturize, humanize and create a high throughput process is offset by any loss of precision and accuracy.

Quantitative Systems Pharmacology for Rare Disease Drug Development.

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.

QSP Designer: Quantitative systems pharmacology modeling with modular biological process map notation and multiple language code generation.

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.

Teaching and learning pharmacology in Brazil before COVID-19 pandemic: a case study in Rio de Janeiro.

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.

"Appraisal of state-of-the-art" The 2021 Distinguished Service Award of the Safety Pharmacology Society: Reflecting on the past to tackle challenges ahead.

This appraisal of state-of-the-art manuscript highlights and expands upon the thoughts conveyed in the lecture of Dr. Jean-Pierre Valentin, recipient of the 2021 Distinguished Service Award of the Safety Pharmacology Society, given on the 2nd December 2021. The article reflects on the strengths, weaknesses, opportunities, and threats that surrounded the evolution of safety and secondary pharmacology over the last 3 decades with a particular emphasis on pharmaceutical drug development delivery, scientific and technological innovation, complexities of regulatory framework and people leadership and development. The article further built on learnings from past experiences to tackle constantly emerging issues and evolving landscape whilst being cognizant of the challenges facing these disciplines in the broader drug development and societal context.

Achieving big with small: quantitative clinical pharmacology tools for drug development in pediatric rare diseases.

Pediatric populations represent a major fraction of rare diseases and compound the intrinsic challenges of pediatric drug development and drug development for rare diseases. The intertwined complexities of pediatric and rare disease populations impose unique challenges to clinical pharmacologists and require integration of novel clinical pharmacology and quantitative tools to overcome multiple hurdles during the discovery and development of new therapies. Drug development strategies for pediatric rare diseases continue to evolve to meet the inherent challenges and produce new medicines. Advances in quantitative clinical pharmacology research have been a key component in advancing pediatric rare disease research to accelerate drug development and inform regulatory decisions. This article will discuss the evolution of the regulatory landscape in pediatric rare diseases, the challenges encountered during the design of rare disease drug development programs and will highlight the use of innovative tools and potential solutions for future development programs.

Validación de la utilidad de la guía farmacológica Ostomecum en formato de software de aplicación / Validation of the utility of the OSTOMECUM pharmacological guidelines in application software format

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)

Ancianos con trastorno adaptativo en la interconsulta de psicogeriatría: perfil clínico y abordaje terapéutico / Elderly patients with adaptive disorder diagnosis in psychogeriatry interconsultation: clinical profile and therapeutic approach

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)

Machine learning for synergistic network pharmacology: a comprehensive overview.

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.