Clinical Pharmacology Strategies for Bispecific Antibody Development: Learnings from FDA-Approved Bispecific Antibodies in Oncology.

Bispecific antibodies, by enabling the targeting of more than one disease-associated antigen or engaging immune effector cells, have both advantages and challenges compared with a combination of two different biological products. As of December 2023, there are 11 U.S. Food and Drug Administration-approved BsAb products on the market. Among these, 9 have been approved for oncology indications, and 8 of these are CD3 T-cell engagers. Clinical pharmacology strategies, including dose-related strategies, are critical for bispecific antibody development. This analysis reviewed clinical studies of all approved bispecific antibodies in oncology and identified dose-related perspectives to support clinical dose optimization and regulatory approvals, particularly in the context of the Food and Drug Administration's Project Optimus: (1) starting doses and dose ranges in first-in-human studies; (2) dose strategies including step-up doses or full doses for recommended phase 2 doses or dose level(s) used for registrational intent; (3) restarting therapy after dose delay; (4) considerations for the introduction of subcutaneous doses; (5) body weight vs. flat dosing strategy; and (6) management of immunogenicity. The learnings arising from this review are intended to inform successful strategies for future bispecific antibody development.

Clinical Pharmacology Considerations for the "Off-the-Shelf" Allogeneic Cell Therapies.

Autologous chimeric antigen receptor T-cell (CAR-T) therapies have garnered unprecedented clinical success with multiple regulatory approvals for the treatment of various hematological malignancies. However, there are still several clinical challenges that limit their broad utilization for aggressive disease conditions. To address some of these challenges, allogeneic cell therapies are evaluated as an alternative approach. As compared with autologous products, they offer several advantages, such as a more standardized "off the shelf" product, reduced manufacturing complexity, and no requirement of bridging therapy. As with autologous CAR-T therapies, allogeneic cell therapies also present clinical pharmacology challenges due to their in vivo living nature, unique pharmacokinetics or cellular kinetics (CKs), and complex dose-exposure-response relationships that are impacted by various patient- and product-related factors. On top of that, allogeneic cell therapies present additional unique challenges, including attenuated in vivo persistence and graft-vs.-host disease risk as compared with autologous counterparts. This review draws comparison between autologous and allogeneic cell therapies, summarizing key engineering aspects unique to allogeneic cell therapy. Clinical pharmacology learnings from emerging clinical data of allogeneic cell therapy programs are also highlighted, with particular emphasis on CK, dose-exposure-response relationship, lymphodepletion regimen, repeat dosing, and patient- and product-related factors that can impact CK and patient outcomes. There are specific unique challenges and opportunities arising from the development of allogeneic cell therapies, especially in optimizing lymphodepletion and establishing a regimen for repeat dosing. This review highlights how clinical pharmacologists are well positioned to help address these challenges by leveraging novel clinical pharmacology and modeling and simulation approaches.

EXOGENOUS Sex Hormones and Sex Hormone Receptor Modulators in COVID-19: Rationale and Clinical Pharmacology Considerations.

Male patients with coronavirus disease 2019 (COVID-19) fare much worse than female patients in COVID-19 severity and mortality according to data from several studies. Because of this sex disparity, researchers hypothesize that the use of exogenous sex hormone therapy and sex hormone receptor modulators might provide therapeutic potential for patients with COVID-19. Repurposing approved drugs or drug candidates at late-stage clinical development could expedite COVID-19 therapy development because their clinical formulation, routes of administration, dosing regimen, clinical pharmacology, and potential adverse events have already been established or characterized in humans. A number of exogenous sex hormones and sex hormone receptor modulators are currently or will be under clinical investigation for COVID-19 therapy. In this review, we discuss the rationale for exogenous sex hormones and sex hormone receptor modulators in COVID-19 treatment, summarize ongoing and planned clinical trials, and discuss some of the clinical pharmacology considerations on clinical study design. To inform clinical study design and facilitate the clinical development of exogenous sex hormones and sex hormone receptor modulators for COVID-19 therapy, clinical investigators should pay attention to clinical pharmacology factors, such as dosing regimen, special populations (i.e., geriatrics, pregnancy, lactation, and renal/hepatic impairment), and drug interactions.

Quantitative Clinical Pharmacology Supports the Bridging From i.v. Dosing and Approval of s.c. Rituximab in B-Cell Hematological Malignancies.

A fixed-dose subcutaneous (s.c.) formulation of the anti-CD20 antibody, rituximab, has been developed to address safety, infusion time, and patient comfort concerns relating to intravenous (i.v.) dosing, and has been approved based upon a pharmacokinetic (PK)-clinical bridging strategy, which demonstrated noninferiority of s.c. vs. i.v. dosing in malignancies, including follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL). A clinical development plan was undertaken to identify rituximab s.c. doses achieving noninferior exposure to rituximab i.v., and to confirm PK-clinical bridging, with the same efficacy and similar safety. This drew upon data from 1,579 patients with FL, CLL, or diffuse large B-cell lymphoma in 5 clinical studies, and showed minimum steady-state serum concentration (Ctrough ) as the most appropriate exposure bridging measure. Population PK models were developed, simulations were run using covariates and PK parameters from clinical studies, and exposure-efficacy and -safety analyses performed. Population PKs showed a two-compartment model with time-dependent and -independent clearances. Clearance and volume were predominantly influenced by body surface area; disposition and elimination were similar for the s.c. and i.v. formulations. After s.c. administration, patients with FL and CLL achieved noninferior exposures to i.v. dosing. Overall, rituximab exposure and route of administration did not influence clinical responses in patients with FL or CLL, and there was no association between exposure and safety events. Ctrough was shown to be an effective pharmacologic-clinical bridging parameter for rituximab in patients with FL or CLL. Clinically effective exposures are achieved with either s.c. or i.v. dosing.

Clinical pharmacology strategies in supporting drug development and approval of antibody-drug conjugates in oncology.

Antibody-drug conjugates (ADCs) are important molecular entities in the treatment of cancer. These conjugates combine the target specificity of monoclonal antibodies with the potent anti-cancer activity of small-molecule therapeutics. The complex structure of ADCs poses unique challenges to characterize the drug's pharmacokinetics (PKs) and pharmacodynamics (PDs) since it requires a quantitative understanding of the PK and PD properties of multiple different molecular species (e.g., ADC conjugate, total antibody and unconjugated cytotoxic drug). As a result, clinical pharmacology strategy of an ADC is rather unique and dependent on the linker/cytotoxic drug technology, heterogeneity of the ADC, PK and safety/efficacy profile of the specific ADC in clinical development. In this review, we summarize the clinical pharmacology strategies in supporting development and approval of ADCs using the approved ADCs as specific examples to illustrate the customized approach to clinical pharmacology assessments in their clinical development.

Future avenues for Alzheimer's disease detection and therapy: liquid biopsy, intracellular signaling modulation, systems pharmacology drug discovery.

When Alzheimer's disease (AD) disease-modifying therapies will be available, global healthcare systems will be challenged by a large-scale demand for clinical and biological screening. Validation and qualification of globally accessible, minimally-invasive, and time-, cost-saving blood-based biomarkers need to be advanced. Novel pathophysiological mechanisms (and related candidate biomarkers) - including neuroinflammation pathways (TREM2 and YKL-40), axonal degeneration (neurofilament light chain protein), synaptic dysfunction (neurogranin, synaptotagmin, α-synuclein, and SNAP-25) - may be integrated into an expanding pathophysiological and biomarker matrix and, ultimately, integrated into a comprehensive blood-based liquid biopsy, aligned with the evolving ATN + classification system and the precision medicine paradigm. Liquid biopsy-based diagnostic and therapeutic algorithms are increasingly employed in Oncology disease-modifying therapies and medical practice, showing an enormous potential for AD and other brain diseases as well. For AD and other neurodegenerative diseases, newly identified aberrant molecular pathways have been identified as suitable therapeutic targets and are currently investigated by academia/industry-led R&D programs, including the nerve-growth factor pathway in basal forebrain cholinergic neurons, the sigma1 receptor, and the GTPases of the Rho family. Evidence for a clinical long-term effect on cognitive function and brain health span of cholinergic compounds, drug candidates for repositioning programs, and non-pharmacological multidomain interventions (nutrition, cognitive training, and physical activity) is developing as well. Ultimately, novel pharmacological paradigms, such as quantitative systems pharmacology-based integrative/explorative approaches, are gaining momentum to optimize drug discovery and accomplish effective pathway-based strategies for precision medicine. This article is part of the special issue on 'The Quest for Disease-Modifying Therapies for Neurodegenerative Disorders'.

Principios de estadística Bayesiana y su relación con la farmacocinética aplicada / Principles of Bayesian statistics and its relationship with applied pharmacokinetics

La metodología estadística Bayesiana permite, si se conoce la probabilidad poblacional de que un suceso ocurra, modificar su valor cuando se dispone de nueva información individual. Aunque las metodologías Bayesiana y frecuentista (clásica) tienen idénticos campos de aplicación, la primera se aplica cada vez más en investigación científica y análisis de big data. En la farmacoterapia moderna, la farmacocinética clínica ha sido responsable de la expansión de la monitorización, facilitada por desarrollos técnico-analíticos y matemático-estadísticos. La farmacocinética poblacional ha permitido identificar y cuantificar las características fisiopatológicas y de tratamiento en una población de pacientes determinada, en particular en pediatría y neonatología, y otros grupos vulnerables, explicando la variabilidad farmacocinética interindividual. Asimismo, la estimación Bayesiana resulta importante como herramienta estadística aplicada en programas informáticos de optimización farmacoterapéutica cuando la monitorización farmacológica se basa en la interpretación farmacocinética clínica. Aunque con ventajas y limitaciones, la optimización farmacoterapéutica basada en la estimación Bayesiana es cada vez más usada en la actualidad, siendo el método de referencia. Esto es particularmente conveniente para la práctica clínica de rutina debido al limitado número de muestras requeridas por parte del paciente, y a la flexibilidad en cuanto a los tiempos de muestreo de sangre para cuantificación de fármacos. Así, la aplicación de los principios Bayesianos a la práctica de la farmacocinética clínica resulta en la mejora de la atención farmacoterapéutica.

If one knows the probability of an event occurring in a population, Bayesian statistics allows mo difying its value when there is new individual information available. Although the Bayesian and frequentist (classical) methodologies have identical fields of application, the first one is increasin gly applied in scientific research and big data analysis. In modern pharmacotherapy, clinical phar macokinetics has been used for the expansion of monitoring, facilitated by technical-analytical and mathematical-statistical developments. Population pharmacokinetics has allowed the identification and quantification of pathophysiological and treatment characteristics in a specific patient popu lation, especially in the pediatric and neonatal population and other vulnerable groups, explaining interindividual variability. Likewise, Bayesian estimation is important as a statistical tool applied in pharmacotherapy optimization software when pharmacological monitoring is based on clinical phar macokinetic interpretation. With its advantages and despite its limitations, pharmacotherapeutic op timization based on Bayesian estimation is increasingly used, becoming the reference method today. This characteristic is particularly convenient for routine clinical practice due to the limited number of samples required from the patient and the flexibility it shows regarding blood sampling times for drug quantification. Therefore, the application of Bayesian principles to the practice of clinical phar macokinetics has led to the improvement of pharmacotherapeutic care.

Identification of Target Genes Related to Sulfasalazine in Triple-Negative Breast Cancer Through Network Pharmacology.

BACKGROUND The anti-inflammatory drug sulfasalazine (SAS) has been confirmed to inhibit the growth of triple-negative breast cancer (TNBC), but the mechanism is not clear. The aim of this study was to use network pharmacology to find relevant pathways of SAS in TNBC patients. MATERIAL AND METHODS Through screening of the GeneCards, CTD, and ParmMapper databases, potential genes related to SAS and TNBC were identified. In addition, gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed using the R programming language. Protein-protein interaction networks were constructed using Cytoscape. The Kaplan-Meier plotter screened genes related to TNBC prognosis. TNBC patient gene expression profiles and clinical data were downloaded from The Cancer Genome Atlas database. A heatmap was generated using the R programming language that presents the expression of potential target genes in patients with TNBC. RESULTS Eighty potential target genes were identified through multiple databases. The bioinformatical analyses predicted the interrelationships, potential pathways, and molecular functions of the genes from multiple aspects, which are associated with physiological processes such as the inflammatory response, metabolism of reactive oxygen species (ROS), and regulation of proteins in the matrix metalloproteinase (MMP) family. Survival analysis showed that 12 genes were correlated with TNBC prognosis. Heatmapping showed that genes such as those encoding members of the MMP family were differentially expressed in TNBC tissues and normal tissues. CONCLUSIONS Our analysis revealed that the main reasons for the inhibitory effect of SAS on TNBC cells may be inhibition of the inflammatory response and MMP family members and activation of ROS.

Medidas y recomendaciones en los estudios de farmacología clínica durante la pandemia COVID-19 / Measures and recommendations in clinical pharmacology studies during the COVID-19 pandemic

En el marco de la actual situación de emergencia sanitaria, la Administración Nacional de Medicamentos, Alimentos y Tecnología Médica (ANMAT), establece las siguientes medidas y recomendaciones con el objeto de preservar las actividades de los estudios de farmacología clínica (EFC) protegiendo la seguridad y bienestar de los participantes del estudio. Los patrocinadores de los EFC deberán confeccionar un plan de mitigación de riesgo para extremar las medidas tendientes a evitar el contagio y diseminación de COVID-19 así como la saturación del sistema sanitario del país. Este plan deberá quedar debidamente documentado en el archivo de cada estudio y será notificado a los investigadores, centros de investigación, comités de Ética y a la ANMAT. Su aplicación no requiere aprobación previa como modificación sustancial por esta Administración. Algunos estudios podrán requerir modificaciones particulares que no se encuentren reflejadas en el plan de mitigación. Estas deberán ser consensuadas con el equipo de investigación y plasmarse en un documento. Este plan deberá ser presentado por cada patrocinador por única vez ante la ANMAT en un trámite caratulado como "comunicación a DERM" por la plataforma de trámites a distancia (TAD) y esta Administración Nacional tomará conocimiento del mismo. No será necesario esperar a una respuesta de esta Administración para su implementación.

Integration of Omics Data Sources to Inform Mechanistic Modeling of Immune-Oncology Therapies: A Tutorial for Clinical Pharmacologists.

Application of contemporary molecular biology techniques to clinical samples in oncology resulted in the accumulation of unprecedented experimental data. These "omics" data are mined for discovery of therapeutic target combinations and diagnostic biomarkers. It is less appreciated that omics resources could also revolutionize development of the mechanistic models informing clinical pharmacology quantitative decisions about dose amount, timing, and sequence. We discuss the integration of omics data to inform mechanistic models supporting drug development in immuno-oncology. To illustrate our arguments, we present a minimal clinical model of the Cancer Immunity Cycle (CIC), calibrated for non-small cell lung carcinoma using tumor microenvironment composition inferred from transcriptomics of clinical samples. We review omics data resources, which can be integrated to parameterize mechanistic models of the CIC. We propose that virtual trial simulations with clinical Quantitative Systems Pharmacology platforms informed by omics data will be making increasing impact in the development of cancer immunotherapies.

Quantitative Systems Pharmacology and Physiologically-Based Pharmacokinetic Modeling With mrgsolve: A Hands-On Tutorial.

mrgsolve is an open-source R package available on the Comprehensive R Archive Network. It combines R and C++ coding for simulation from hierarchical, ordinary differential equation-based models. Its efficient simulation engine and integration into a parallelizable, R-based workflow makes mrgsolve a convenient tool both for simple and complex models and thus is ideal for physiologically-based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) model. This tutorial will first introduce the basics of the mrgsolve simulation workflow, including model specification, the introduction of interventions (dosing events) into the simulation, and simulated results postprocessing. An applied simulation example is then presented using a PBPK model for voriconazole, including a model validation step against adult and pediatric data sets. A final simulation example is then presented using a previously published QSP model for mitogen-activated protein kinase signaling in colorectal cancer, illustrating population simulation of different combination therapies.

Is There Any Room for Pharmacometrics With Immuno-Oncology Drugs? Input from the EORTC-PAMM Course on Preclinical and Early-phase Clinical Pharmacology.

As part of the Pharmacology & Molecular Mechanisms (PAMM) Group, European Organization for Research and Treatment on Cancer (EORTC) 2019 winter Meeting Educational sessions, special focus has been placed on strategies to be undertaken to reduce the attrition rate when developing immune-oncology drugs. Immune checkpoint inhibitors have been game-changing drugs in several settings over the past decade such as melanoma and lung cancer. However, during the last years a rising number of studies failing to further improve clinical outcome in patients with cancer was recorded. Extensive pharmacometrics such as pharmacokinetics/pharmacodynamics modeling support should help to overcome the current glass ceiling that has apparently been reached with immuno-oncology drugs (IOD). In particular, it should help in the issue of setting up combinatorial regimen (i.e. combining immune checkpoint inhibitors with cytotoxics, anti-angiogenesis or targeted therapies) that can no longer be addressed when following standard trial-and-error approaches, but rather by using mathematical-derived algorithms as decision-making tools by investigators for rational design. In routine clinical setting, developing therapeutic drug monitoring of immune checkpoint inhibitors with adaptive dosing strategies has been a long-neglected strategy. Still, substantial improvements might be achieved using dedicated tools for precision medicine and personalized medicine in immunotherapy.

A Brief Guide to Performing Pharmacological Studies In Vitro: Reflections from the EORTC-PAMM Course "Preclinical and Early-phase Clinical Pharmacology".

One aim of cell-based in vitro assays is to identify the best drug candidate to develop using the best tumor cell model. This is challenging in every anticancer drug discovery process. Briefly, we summarize the parameters to be taken into account when performing in vitro cell assays, in order to obtain reliable and reproducible results, which was fundamentally discussed by lecturers at the educational course on preclinical and early-phase clinical pharmacology studies, at the 40th Winter Meeting of the Pharmacology and Molecular Mechanisms Group of the European Organization for Research and Treatment of Cancer. Moreover, specific cellular sensitivity tests are described. In addition to monolayer in vitro cell models for the screening of new potential candidate drugs, three-dimensional tumor/cell tissue models are emerging as new pre-clinical tools that more closely reflect the in vivo microenvironment. Therefore, the use of different in vitro models for drug screening can enhance the predictability and reliability of pre-clinical drug-discovery phases and target validation.

[Medicinal treatment of pain in the elderly: 10 tips from clinical pharmacology]]. / Medicamenteuze behandeling van pijn bij ouderen.

Although many older patients use analgesics from time to time, little is known about the safety and efficacy of these medicines in older people. The balance between effectiveness and side effects may be totally different from what is expected. Physiological and pathophysiological changes can cause the pharmacokinetics and pharmacodynamics of medicines to change. Drug-drug interactions can enhance or decrease the effects of medicines. In frail older people with multimorbidity and polypharmacy in particular, these changes may lead to a higher risk of side effects and a decline in the analgesic effect. The vulnerability of many older people means that even when pain is adequately controlled, concomitant side effects of medicines can have a big negative impact on their general functioning. Pharmacological pain management in older people requires specific competences and knowledge. In this article we present 10 tips for pain management in older people from a pharmacological perspective.

An opportunity for clinical pharmacology trained physicians to improve patient drug safety: A retrospective analysis of adverse drug reactions in teenagers.

Background: Adverse drug reactions (ADRs) are a major cause of hospital admissions, prolonged hospital stays, morbidity, and drug-related mortality. In this study, we sought to identify the most frequently reported medications and associated side effects in adolescent-aged patients in an effort to prioritize clinical pharmacology consultation efforts for hospitals seeking to improve patient safety.   Methods: Quarterly reported data were obtained from the United States Food and Drug Administration Adverse Events Reporting System (FAERS) from the third quarter of 2014 and ending in the third quarter of 2017. We then used the GeneCards database to map the pharmacogenomic biomarkers associated with the most reported FAERS drugs. Data homogenization and statistics analysis were all conducted in R for statistical programming. Results: We identified risperidone (10.64%) as the compound with the most reported ADRs from all reported cases. Males represented 90.1% of reported risperidone cases with gynecomastia being the most reported ADR. Ibuprofen OR=188 (95% CI, 105.0000 - 335.000) and quetiapine fumarate OR=116 (95% CI, 48.4000 - 278.000) were associated with the highest odds of completed suicide in teenagers. Ondansetron hydrochloride OR=7.12 (95% CI, 1.59 - 31.9) resulted in the highest odds of pneumothorax. Lastly, olanzapine (8.96%) represented the compound with the most reported drug-drug interactions cases, while valproic acid OR=221 (95% CI, 93.9000 - 522.000) was associated with the highest odds of drug-drug interactions. Conclusion: Despite any data limitations, physicians prescribing risperidone in males should be aware of the high rates of adverse drug events and an alternative psychotropic should be considered in male patients. Further, patients with a history of pneumothorax or genetically predisposed to pneumothorax should be considered for an alternative antiemetic to ondansetron hydrochloride, due to increased odds associated with the drug and adverse event.

Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time-to-Event Analysis.

Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time-to-event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high-dimensional data featured by a large number of predictor variables. Our results showed that ML-based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high-dimensional data. The prediction performances of ML-based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML-based methods provide a powerful tool for time-to-event analysis, with a built-in capacity for high-dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function.