Proceedings of a Workshop: Precision Dosing: Defining the Need and Approaches to Deliver Individualized Drug Dosing in the Real-World Setting

Precision Dosing in Presence of Multiobjective Therapies by Integrating Reinforcement Learning and PK-PD Models: Application to Givinostat Treatment of Polycythemia Vera

Precision dosing aims to optimize and customize pharmacological treatment at the individual level. The integration of pharmacometric models with Reinforcement Learning (RL) algorithms is currently under investigation to support the personalization of adaptive dosing therapies. In this study, this hybrid technique is applied to the real multiobjective precision dosing problem of givinostat treatment in polycythemia vera (PV) patients. PV is a chronic myeloproliferative disease with an overproduction of platelets (PLT), white blood cells (WBC), and hematocrit (HCT). The therapeutic goal is to simultaneously normalize the levels of these efficacy/safety biomarkers, thus inducing a complete hematological response (CHR). An RL algorithm, Q-Learning (QL), was integrated with a PK-PD model describing the givinostat effect on PLT, WBC, and HCT to derive both an adaptive dosing protocol (QLpop-agent) for the whole population and personalized dosing strategies by coupling a specific QL-agent to each patient (QLind-agents). QLpop-agent learned a general adaptive dosing protocol that achieved a similar CHR rate (77% vs. 83%) when compared to the actual givinostat clinical protocol on 10 simulated populations. Treatment efficacy and safety increased with a deeper dosing personalization by QLind-agents. These QL-based patient-specific adaptive dosing rules outperformed both the clinical protocol and QLpop-agent by reaching the CHR in 93% of the test patients and completely avoided severe toxicities during the whole treatment period. These results confirm that RL and PK-PD models can be valid tools for supporting adaptive dosing strategies as interesting performances were achieved in both learning a general set of rules and in customizing treatment for each patient.

Therapeutic Drug Monitoring of Biologics: Current Practice, Challenges and Opportunities - a Workshop Report

Therapeutic drug monitoring (TDM) for dose modification of biologics has the potential to improve patient outcomes. The US Food and Drug Administration (FDA) and the American Association of Pharmaceutical Scientists (AAPS) hosted the first US-based public workshop on TDM of biologics with contributions from a broad array of interested parties including healthcare providers, clinical pharmacologists, test developers, bioanalysis and immunogenicity scientists, health economics and outcomes research (HEOR) experts and regulators. The key insight was that despite a body of evidence to support TDM in certain therapeutic areas, there remain substantial challenges to widespread clinical implementation. There is a lack of consensus regarding the integration of TDM in clinical guidelines, and a lack of consensus on the cost-effectiveness of TDM; both factors contribute to the difficulty that healthcare providers face in obtaining reimbursement for TDM (both coverage of testing itself, and coverage of potential dosing modifications). The HEOR experts outlined alternative routes to obtaining reimbursement and suggested advocating for changes in coverage policies to promote TDM use in the clinic. Reaching alignment across policy makers, patients and advocacy groups, payers, and healthcare providers, on specific treatment settings where TDM will be clearly beneficial, was identified as an important step to advancing TDM implementation for the benefit of patients.

Barriers and Facilitators for Bringing Model-Informed Precision Dosing to the Patient's Bedside: A Systematic Review

Model-informed precision dosing (MIPD) utilizes mathematical models to predict optimal medication doses for a specific patient or patient population. However, the factors influencing the implementation of MIPD have not been fully elucidated, hindering its widespread use in clinical practice. A systematic review was conducted in PubMed from inception to December 2022, aiming to identify barriers and facilitators for the implementation of MIPD into patient care. Articles with a focus on implementation of MIPD were eligible for this review. After screening titles and abstracts, full articles investigating the clinical implementation of MIPD were included for data extraction. Of 790 records identified, 15 publications were included. A total of 72 barriers and facilitators across seven categories were extracted through a hybrid thematic analysis. Barriers comprised limited data for model validation, unclear regulatory pathways for model endorsement and additional drug level measurements required for certain types of MIPD. Facilitators encompassed the development of user-friendly MIPD tools continuously updated based on user feedback and data. Collaborative efforts among diverse stakeholders for model validation and implementation, along with education of end-users, may promote the utilization of MIPD in patient care. Despite ongoing challenges, this systematic review revealed various strategies to facilitate the clinical implementation of MIPD.

Beyond Population-Level Targets for Drug Concentrations: Precision Dosing Needs Individual-Level Targets that Include Superior Biomarkers of Drug Responses

The purpose of precision dosing is to increase the chances of therapeutic success in individual patients. This is achieved in practice by adjusting doses to reach precision dosing targets determined previously in relevant populations, ideally with robust supportive evidence showing improved clinical outcomes compared with standard dosing. But is this implicit assumption of translatable population-level precision dosing targets correct and the best for all patients? In this review, the types of precision dosing targets and how they are determined are outlined, problems with the translatability of these targets to individual patients are identified, and ways forward to address these challengers are proposed. Achieving improved clinical outcomes to support precision dosing over standard dosing is currently hampered by applying population-level targets to all patients. Just as "one-dose-fits-all" may be an inappropriate philosophy for drug treatment overall, a "one-target-fits-all" philosophy may limit the broad clinical benefits of precision dosing. Defining individual-level precision dosing targets may be needed for greatest therapeutic success. Superior future precision dosing targets will integrate several biomarkers that together account for the multiple sources of drug response variability.

Reinforcement Learning and PK-PD Models Integration to Personalize the Adaptive Dosing Protocol of Erdafitinib in Patients with Metastatic Urothelial Carcinoma

The integration of pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulations with artificial intelligence/machine learning algorithms is one of the most attractive areas of the pharmacometric research. These hybrid techniques are currently under investigation to perform several tasks, among which precision dosing. In this scenario, this paper presents and evaluates a new framework embedding PK-PD models into a reinforcement learning (RL) algorithm, Q-learning (QL), to personalize pharmacological treatment. Each patient is represented with a set of PK-PD parameters and has a personal QL agent which optimizes the individual treatment. In the training phase, leveraging PK-PD simulations, the QL agent assesses different actions, defined consistently with the clinical knowledge to consider only plausible dose-adjustments, in order to find the optimal rules. The proposed framework was evaluated to optimize the erdafitinib treatment in patients with metastatic urothelial carcinoma. This drug was approved by the US Food and Drug Administration (FDA) with a dose-adaptive protocol based on monitoring the levels of serum phosphate, which represent a biomarker of both treatment efficacy and toxicity. To evaluate the flexibility of the methodology, a heterogeneous virtual population of 141 patients was generated using an erdafitinib population PK (PopPK)-PD literature model. For each patient, treatment response was simulated by using both QL-optimized protocol and the clinical one. QL agents outperform the approved dose-adaptive rules, increasing more than 10% the efficacy and the safety of treatment at each end point. Results confirm the great potentialities of the integration of PopPK-PD models and RL algorithms to optimize precision dosing tasks.

Biologics, theranostics, and personalized medicine in drug delivery systems

The progress in human disease treatment can be greatly advanced through the implementation of nanomedicine. This approach involves targeted and cell-specific therapy, controlled drug release, personalized dosage forms, wearable drug delivery, and companion diagnostics. By integrating cutting-edge technologies with drug delivery systems, greater precision can be achieved at the tissue and cellular levels through the use of stimuli-responsive nanoparticles, and the development of electrochemical sensor systems. This precision targeting - by virtue of nanotechnology - allows for therapy to be directed specifically to affected tissues while greatly reducing side effects on healthy tissues. As such, nanomedicine has the potential to transform the treatment of conditions such as cancer, genetic diseases, and chronic illnesses by facilitating precise and cell-specific drug delivery. Additionally, personalized dosage forms and wearable devices offer the ability to tailor treatment to the unique needs of each patient, thereby increasing therapeutic effectiveness and compliance. Companion diagnostics further enable efficient monitoring of treatment response, enabling customized adjustments to the treatment plan. The question of whether all the potential therapeutic approaches outlined here are viable alternatives to current treatments is also discussed. In general, the application of nanotechnology in the field of biomedicine may provide a strong alternative to existing treatments for several reasons. In this review, we aim to present evidence that, although in early stages, fully merging advanced technology with innovative drug delivery shows promise for successful implementation across various disease areas, including cancer and genetic or chronic diseases.

Support to early clinical decisions in drug development and personalised medicine with checkpoint inhibitors using dynamic biomarker-overall survival models

Longitudinal models of biomarkers such as tumour size dynamics capture treatment efficacy and predict treatment outcome (overall survival) of a variety of anticancer therapies, including chemotherapies, targeted therapies, immunotherapies and their combinations. These pharmacological endpoints like tumour dynamic (tumour growth inhibition) metrics have been proposed as alternative endpoints to complement the classical RECIST endpoints (objective response rate, progression-free survival) to support early decisions both at the study level in drug development as well as at the patients level in personalised therapy with checkpoint inhibitors. This perspective paper presents recent developments and future directions to enable wider and robust use of model-based decision frameworks based on pharmacological endpoints.

Precision antiplatelet therapy

A State of the Art lecture titled "Personalizing Antiplatelet Therapy Based on Platelet Turnover and Metabolic Phenotype" was presented by Bianca Rocca at the International Society on Thrombosis and Haemostasis (ISTH) Congress in 2022. Increased variability in drug response may be associated with serious, mechanism-based and off-target side effects, especially in the case of drugs that do not routinely undergo therapeutic drug monitoring, such as antiplatelet drugs or direct oral anticoagulants. Precision pharmacology can be defined as the identification of a drug regimen that maximizes the benefit/risk balance at the level of an individual patient. Key tools for identifying relevant sources of variability and developing precision drug dosing are represented by genetic, biochemical, and pharmacological biomarkers recognized as a valid surrogate or strong predictor of major clinical complications. Pharmacodynamic, pharmacokinetic, and/or disease-related biomarkers are central to identifying the right population to be targeted, characterizing the sources of variability in drug response, guiding precision treatments that maximize benefits and minimize risks, and designing precision dosing trials. Another valuable tool for guiding precision pharmacology is represented by in silico pharmacokinetic/pharmacodynamic models and simulations instructed by real-world data of validated biomarkers. This review critically analyzes the tools for precision dosing and exemplifies conditions in which precision dosing can considerably optimize the efficacy and safety of antiplatelet drugs, namely aspirin and P2Y12 receptor blockers, used alone and in combination. Finally, we summarize relevant new data on this topic presented during the 2022 ISTH Congress.

Platelet thromboxane inhibition by low-dose aspirin in polycythemia vera: Ex vivo and in vivo measurements and in silico simulation

Low-dose aspirin is currently recommended for patients with polycythemia vera (PV), a myeloproliferative neoplasm with increased risk of arterial and venous thromboses. Based on aspirin pharmacodynamics in essential thrombocythemia, a twice-daily regimen is recommended for patients with PV deemed at particularly high thrombotic risk. We investigated the effects of low-dose aspirin on platelet cyclooxygenase activity and in vivo platelet activation in 49 patients with PV, as assessed by serum thromboxane (TX) B2 and urinary TXA2 /TXB2 metabolite (TXM) measurements, respectively. A previously described pharmacokinetic-pharmacodynamic in silico model was used to simulate the degree of platelet TXA2 inhibition by once-daily (q.d.) and twice-daily (b.i.d.) aspirin, and to predict the effect of missing an aspirin dose during q.d. and b.i.d. regimens. Serum TXB2 averaged 8.2 (1.6-54.7) ng/ml and significantly correlated with the platelet count (γ = 0.39) and urinary TXM (γ = 0.52) in multivariable analysis. One-third of aspirin-treated patients with PV displayed less-than-maximal platelet TXB2 inhibition, and were characterized by significantly higher platelet counts and platelet-count corrected serum TXB2 than those with adequate inhibition. Eight patients with PV were sampled again after 12 ± 4 months, and had reproducible serum TXB2 and urinary TXM values. The in silico model predicted complete inhibition of platelet-derived TXB2 by b.i.d. aspirin, a prediction verified in a patient with PV with the highest TXB2 value while on aspirin q.d. and treated short-term with a b.i.d. regimen. In conclusion, one in three patients with PV on low-dose aspirin display less-than-maximal inhibition of platelet TXA2 production. Serum TXB2 measurement can be a valuable option to guide precision dosing of antiplatelet therapy in patients with PV.

Individualized Patient Care Through Model-Informed Precision Dosing: Reflections on Training Future Practitioners

Prior to his passing, Dr. Roger Jelliffe, expressed the need for educating future physicians and clinical pharmacists on the availability of computer-based tools to support dose optimization in patients in stable or unstable physiological states. His perspectives were to be captured in a commentary for the AAPS J with a focus on incorporating population pharmacokinetic (PK)/pharmacodynamic (PD) models that are designed to hit the therapeutic target with maximal precision. Unfortunately, knowing that he would be unable to complete this project, Dr. Jelliffe requested that a manuscript conveying his concerns be completed upon his passing. With this in mind, this final installment of the AAPS J theme issue titled "Alternative Perspectives for Evaluating Drug Exposure Characteristics in a Population - Avoiding Analysis Pitfalls and Pigeonholes" is an effort to honor Dr. Jelliffe's request, conveying his concerns and the need to incorporate modeling and simulation into the training of physicians and clinical pharmacists. Accordingly, Dr. Jelliffe's perspectives have been integrated with those of the other three co-authors on the following topics: the clinical utility of population PK models; the role of multiple model (MM) dosage regimens to identify an optimal dose for an individual; tools for determining dosing regimens in renal dialysis patients (or undergoing other therapies that modulate renal clearance); methods to analyze and track drug PK in acutely ill patients presenting with high inter-occasion variability; implementation of a 2-cycle approach to minimize the duration between blood samples taken to estimate the changing PK in an acutely ill patient and for the generation of therapeutic decisions in advance for each dosing cycle based on an analysis of the previous cycle; and the importance of expressing therapeutic drug monitoring results as 1/variance rather than as the coefficient of variation. Examples showcase why, irrespective of the overall approach, the combination of therapeutic drug monitoring and computer-informed precision dosing is indispensable for maximizing the likelihood of achieving the target drug concentrations in the individual patient.

The case for precision dosing: medical conservatism does not justify inaction

The need for precision dosing has been challenged on the basis of insufficient evidence. Herein, we argue that adequate evidence exists to conduct therapeutic drug monitoring (TDM) and precisely target antibiotic exposures. While achievement of any antibiotic concentration does not guarantee efficacy sans toxicity for any single patient, stochastic control optimizes the probability of achieving favourable responses across patients. We argue that variability in targets (such as the organism's MIC) can be considered with models. That is, complexity alone does not relegate the decision-making framework to 'clinician intuition'. We acknowledge the exposure-response relationships are modified by patient-specific factors (other drugs, baseline organ functional status etc.) and describe how precision dosing can inform clinical decision making rather than protocolize it. Finally, we call for randomized, controlled trials; however, we suggest that these trials are not necessary to make TDM standard of care for multiple classes of antibiotics.

A Systematic Assessment of US Food and Drug Administration Dosing Recommendations For Drug Development Programs Amenable to Response-Guided Titration

A key goal in drug development is optimized dosing for patients. Interactions between drug developers and regulatory scientists throughout development are important for the optimization of dosing and serve as a forum to discuss approaches for optimal dosing, such as precision or individualized dosing. To date, there has not been a systematic assessment of the advice provided by the US Food and Drug Administration (FDA) to drug developers from an individualized dosing perspective. Here, we reviewed FDA recommendations on dose selection for efficacy trials at end-of-phase meetings between the FDA and drug developers for 76 new molecular entities approved between 2013 and 2017 that are considered amenable for an individualized dosing method, response-guided titration. Forty FDA dosing recommendations were identified as specific to dose selection and design of the respective efficacy trials and subsequently: (i) characterized based on if they were supportive of individualized dosing and (ii) compared with dosing regimens used in efficacy trials and labeling at approval to evaluate if FDA recommendations were implemented. Of these 40 recommendations for efficacy trials, 35 (88%) were considered supportive of individualized dosing. Eighteen of these 40 recommendations (45%) were incorporated into efficacy trials and 11 (28%) were incorporated into labeling. This research suggests that early FDA-sponsor interactions can support the study of doses in efficacy trials that may lead to individualized dosing strategies in labeling.