Perspective on model-informed drug development

Model-Informed Radiopharmaceutical Therapy Optimization: A Study on the Impact of PBPK Model Parameters on Physical, Biological, and Statistical Measures in 177Lu-PSMA Therapy

Purpose: To investigate the impact of physiologically based pharmacokinetic (PBPK) parameters on physical, biological, and statistical measures in lutetium-177-labeled radiopharmaceutical therapies (RPTs) targeting the prostate-specific membrane antigen (PSMA). Methods: Using a clinically validated PBPK model, realistic time-activity curves (TACs) for tumors, salivary glands, and kidneys were generated based on various model parameters. These TACs were used to calculate the area-under-the-TAC (AUC), dose, biologically effective dose (BED), and figure-of-merit BED (fBED). The effects of these parameters on radiobiological, pharmacokinetic, time, and statistical features were assessed. Results: Manipulating PBPK parameters significantly influenced AUC, dose, BED, and fBED outcomes across four different BED models. Higher association rates increased AUC, dose, and BED values for tumors, with minimal impact on non-target organs. Increased internalization rates reduced AUC and dose for tumors and kidneys. Higher serum protein-binding rates decreased AUC and dose for all tissues. Elevated tumor receptor density and ligand amounts enhanced uptake and effectiveness in tumors. Larger tumor volumes required dosimetry adjustments to maintain efficacy. Setting the tumor release rate to zero intensified the impact of association and internalization rates, enhancing tumor targeting while minimizing the effects on salivary glands and kidneys. Conclusions: Optimizing PBPK parameters can enhance the efficacy of lutetium-177-labeled RPTs targeting PSMA, providing insights for personalized and effective treatment regimens to minimize toxicity and improve therapeutic outcomes.

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.

More Than a Gut Feeling─A Combination of Physiologically Driven Dissolution and Pharmacokinetic Modeling as a Tool for Understanding Human Gastric Motility

In vivo studies of formulation performance with in vitro and/or in silico simulations are often limited by significant gaps in our knowledge of the interaction between administered dosage forms and the human gastrointestinal tract. This work presents a novel approach for the investigation of gastric motility influence on dosage form performance, by combining biopredictive dissolution tests in an innovative PhysioCell apparatus with mechanistic physiology-based pharmacokinetic modeling. The methodology was based on the pharmacokinetic data from a large (n = 118) cohort of healthy volunteers who ingested a capsule containing a highly soluble and rapidly absorbed drug under fasted conditions. The developed dissolution tests included biorelevant media, varied fluid flows, and mechanical stress events of physiological timing and intensity. The dissolution results were used as inputs for pharmacokinetic modeling that led to the deduction of five patterns of gastric motility and their prevalence in the studied population. As these patterns significantly influenced the observed pharmacokinetic profiles, the proposed methodology is potentially useful to other in vitro-in vivo predictions involving immediate-release oral dosage forms.

A Modern Curriculum for Training Scientists in Model-Informed Drug Development: Progress Report on FDA Grant to Train Regulatory Scientists

Under US Food and Drug Administration (FDA) grant (2U18FD005320-06), the Critical Path Institute (C-Path) and experienced private sector partners collaborated with global health organizations to create didactic video materials in an e-learning format on model-informed drug development (MIDD) topics relevant to a non-modeling audience. Several multinational pharmaceutical companies contributed case studies illustrating the application of the MIDD approach in practice. Training videos were created and divided into several modules: introducing the MIDD landscape for drug development and regulatory science, a review of various model types used for MIDD, discussions of how models inform drug development and regulatory decisions, future goals of MIDD, and discussions on the interconnectedness of models used for MIDD. Examples and vignettes from stakeholders and thought leaders were included. These educational materials fill a gap between academic and "on the job training" for regulators, academic, and industry scientists, delivering insights and value for those performing modeling and non-modelers reviewing the output of modeling and simulation work. A total of 13 hours of video content is currently available. A small panel of FDA reviewers is currently beta-testing the learning management system (LMS). Future efforts for this MIDD training initiative will include expansion of the content via an expanded and diverse faculty, 1:1 online mentorship sessions, and eventually broader access to this resource consistent with an open science approach and curriculum. The MIDD training LMS can accommodate a diverse learning ecosystem; further development may also accommodate different audiences in the future.

Considerations for Industry-Preparing for the FDA Model-Informed Drug Development (MIDD) Paired Meeting Program

A recent industry perspective published in this journal describes the benefits received by drug companies from participation in the MIDD Pilot Program. Along with the primary objectives of supporting good decision-making in drug development, there were substantial savings in time and development costs. Furthermore, many sponsors reported qualitative benefits such as new learnings and clarity on MIDD strategies and methodology that could be applied to other development programs. Based on the success of the Pilot Program, the FDA recently announced the continuation of the MIDD Paired Meeting Program as part of the Prescription Drug User Fee Act (PDUFA VII). In this report, we describe the collective experiences of industry participants in the MIDD Program to date, including all aspects of the process from meeting request submission to follow-up actions. The purpose is to provide future participants with information to optimize the value of the MIDD Program.

Evolution of Drug Development and Regulatory Affairs: The Demonstrated Power of Artificial Intelligence

PURPOSE

Artificial intelligence (AI) refers to technology capable of mimicking human cognitive functions and has important applications across all sectors and industries, including drug development. This has considerable implications for the regulation of drug development processes, as it is expected to transform both the way drugs are brought to market and the systems through which this process is controlled. There is currently insufficient evidence in published literature of the real-world applications of AI. Therefore, this narrative review investigated, collated, and elucidated the applications of AI in drug development and its regulatory processes.

METHODS

A narrative review was conducted to ascertain the role of AI in streamlining drug development and regulatory processes.

FINDINGS

The findings of this review revealed that machine learning or deep learning, natural language processing, and robotic process automation were favored applications of AI. Each of them had considerable implications on the operations they were intended to support. Overall, the AI tools facilitated access and provided manageability of information for decision-making across the drug development lifecycle. However, the findings also indicate that additional work is required by regulatory authorities to set out appropriate guidance on applications of the technology, which has critical implications for safety, regulatory process workflow and product development costs.

IMPLICATIONS

AI has adequately proven its utility in drug development, prompting further investigations into the translational value of its utility based on cost and time saved for the delivery of essential drugs.

In silico trial for the assessment of givinostat dose adjustment rules based on the management of key hematological parameters in polycythemia vera patients

Polycythemia vera (PV) is a chronic myeloproliferative neoplasm characterized by excessive levels of platelets (PLT), white blood cells (WBC), and hematocrit (HCT). Givinostat (ITF2357) is a potent histone-deacetylase inhibitor that showed a good safety/efficacy profile in PV patients during phase I/II studies. A phase III clinical trial had been planned and an adaptive dosing protocol had been proposed where givinostat dose is iteratively adjusted every 28 days (one cycle) based on PLT, WBC, and HCT. As support, a simulation platform to evaluate and refine the proposed givinostat dose adjustment rules was developed. A population pharmacokinetic/pharmacodynamic model predicting the givinostat effects on PLT, WBC, and HCT in PV patients was developed and integrated with a control algorithm implementing the adaptive dosing protocol. Ten in silico trials in ten virtual PV patient populations were simulated 500 times. Considering an eight-treatment cycle horizon, reducing/increasing the givinostat daily dose by 25 mg/day step resulted in a higher percentage of patients with a complete hematological response (CHR), that is, PLT ≤400 × 109 /L, WBC ≤10 × 109 /L, and HCT < 45% without phlebotomies in the last three cycles, and a lower percentage of patients with grade II toxicity events compared with 50 mg/day adjustment steps. After the eighth cycle, 85% of patients were predicted to receive a dose ≥100 mg/day and 40.90% (95% prediction interval = [34, 48.05]) to show a CHR. These results were confirmed at the end of 12th, 18th, and 24th cycles, showing a stability of the response between the eighth and 24th cycles.

Prospective approaches to gene therapy computational modeling - spotlight on viral gene therapy

Clinical studies have found there still exists a lack of gene therapy dose-toxicity and dose-efficacy data that causes gene therapy dose selection to remain elusive. Model informed drug development (MIDD) has become a standard tool implemented throughout the discovery, development, and approval of pharmaceutical therapies, and has the potential to inform dose-toxicity and dose-efficacy relationships to support gene therapy dose selection. Despite this potential, MIDD approaches for gene therapy remain immature and require standardization to be useful for gene therapy clinical programs. With the goal to advance MIDD approaches for gene therapy, in this review we first provide an overview of gene therapy types and how they differ from a bioanalytical, formulation, route of administration, and regulatory standpoint. With this biological and regulatory background, we propose how MIDD can be advanced for AAV-based gene therapies by utilizing physiological based pharmacokinetic modeling and quantitative systems pharmacology to holistically inform AAV and target protein dynamics following dosing. We discuss how this proposed model, allowing for in-depth exploration of AAV pharmacology, could be the key the field needs to treat these unmet disease populations.

Virtual twin for healthcare management

Healthcare is increasingly fragmented, resulting in escalating costs, patient dissatisfaction, and sometimes adverse clinical outcomes. Strategies to decrease healthcare fragmentation are therefore attractive from payer and patient perspectives. In this commentary, a patient-centered smart phone application called Virtual Twin for Healthcare Management (VTHM) is proposed, including its organizational layout, basic functionality, and potential clinical applications. The platform features a virtual twin hub that displays the body and its health data. This is a physiologically based human model that is "virtualized" for the patient based on their unique genetic, molecular, physiological, and disease characteristics. The spokes of the system are a full service and interoperable electronic-health record, accessible to healthcare providers with permission on any device with internet access. Theoretical case studies based on real scenarios are presented to show how VTHM could potentially improve patient care and clinical efficiency. Challenges that must be overcome to turn VTHM into reality are also briefly outlined. Notably, the VTHM platform is designed to operationalize current and future precision medicine initiatives, such as access to molecular diagnostic results, pharmacogenomics-guided prescribing, and model-informed precision dosing.

Longitudinal Meta-Analysis of Historical Parkinson's Disease Trials to Inform Future Trial Design

BACKGROUND

The outcome of clinical trials in neurodegeneration can be highly uncertain due to the presence of a strong placebo effect.

OBJECTIVES

To develop a longitudinal model that can enhance the success of future Parkinson's disease trials by quantifying trial-to-trial variations in placebo and active treatment response.

METHODS

A longitudinal model-based meta-analysis was conducted on the total score of Unified Parkinson's Disease Rating Scale (UPDRS) Parts 1, 2, and 3. The analysis included aggregate data from 66 arms (observational [4], placebo [28], or investigational-drug-treated [34]) from 4 observational studies and 17 interventional trials. Inter-study variabilities in key parameters were estimated. Residual variability was weighted by the size of study arms.

RESULTS

The baseline total UPDRS was estimated to average at 24.5 points. Disease score was estimated to worsen by 3.90 points/year for the duration of the treatments; whilst notably, arms with a lower baseline progressed faster. The model captured the transient nature of the placebo response and sustained symptomatic drug effect. Both placebo and drug effects peaked within 2 months; although, 1 year was needed to observe the full treatment difference. Across these studies, the progression rate varied by 59.4%, the half-life for offset of placebo response varied by 79.4%, and the amplitude for drug effect varied by 105.3%.

CONCLUSION

The longitudinal model-based meta-analysis describes UPDRS progression rate, captures the dynamics of the placebo response, quantifies the effect size of the available therapies, and sets the expectation of uncertainty for future trials. The findings provide informative priors to enhance the rigor and success of future trials of promising agents, including potential disease modifiers. © 2023 GSK. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

An AI Approach to Generating MIDD Assets Across the Drug Development Continuum

Model-informed drug development involves developing and applying exposure-based, biological, and statistical models derived from preclinical and clinical data sources to inform drug development and decision-making. Discrete models are generated from individual experiments resulting in a single model expression that is utilized to inform a single stage-gate decision. Other model types provide a more holistic view of disease biology and potentially disease progression depending on the appropriateness of the underlying data sources for that purpose. Despite this awareness, most data integration and model development approaches are still reliant on internal (within company) data stores and traditional structural model types. An AI/ML-based MIDD approach relies on more diverse data and is informed by past successes and failures including data outside a host company (external data sources) that may enhance predictive value and enhance data generated by the sponsor to reflect more informed and timely experimentation. The AI/ML methodology also provides a complementary approach to more traditional modeling efforts that support MIDD and thus yields greater fidelity in decision-making. Early pilot studies support this assessment but will require broader adoption and regulatory support for more evidence and refinement of this paradigm. An AI/ML-based approach to MIDD has the potential to transform regulatory science and the current drug development paradigm, optimize information value, and increase candidate and eventually product confidence with respect to safety and efficacy. We highlight early experiences with this approach using the AI compute platforms as representative examples of how MIDD can be facilitated with an AI/ML approach.

Mechanism-based pharmacokinetic and pharmacodynamic modeling for bispecific antibodies: challenges and opportunities

INTRODUCTION

Unlike conventional antibodies, bispecific antibodies (bsAbs) are engineered antibody- or antibody fragment-based molecules that can simultaneously recognize two different epitopes or antigens. Over the past decade, there has been an explosion of bsAbs being developed across therapeutic areas. Development of bsAbs presents unique challenges and mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) modeling has served as a powerful tool to optimize their development and realize their clinical utility.

AREAS COVERED

In this review, the guiding principles and case examples of how fit-for-purpose, mechanism-based PK/PD models have been applied to answer questions commonly encountered in bsAb development are presented. Such models characterize the key pharmacological elements of bsAbs, and they can be utilized for model-informed drug development. We also include the discussion of challenges, knowledge gaps and future direction for such models.

EXPERT OPINION

Mechanistic PK/PD modeling is a powerful tool to support the development of bsAbs. These models can be extrapolated to predict treatment outcomes based on mechanisms of action (MoA) and clinical observations to form positive learn-and-confirm cycles during drug development, due to their abilities to differentiate system- and drug-specific parameters. Meanwhile, the models should keep being adapted according to novel drug design and MoA, providing continuous opportunities for model-informed drug development.

A Mechanistic Pharmacodynamic Modeling Framework for the Assessment and Optimization of Proteolysis Targeting Chimeras (PROTACs)

The field of targeted protein degradation is growing exponentially. Yet, there is an unmet need for pharmacokinetic/pharmacodynamic models that provide mechanistic insights, while also being practically useful in a drug discovery setting. Therefore, we have developed a comprehensive modeling framework which can be applied to experimental data from routine projects to: (1) assess PROTACs based on accurate degradation metrics, (2) guide compound optimization of the most critical parameters, and (3) link degradation to downstream pharmacodynamic effects. The presented framework contains a number of first-time features: (1) a mechanistic model to fit the hook effect in the PROTAC concentration-degradation profile, (2) quantification of the role of target occupancy in the PROTAC mechanism of action and (3) deconvolution of the effects of target degradation and target inhibition by PROTACs on the overall pharmacodynamic response. To illustrate applicability and to build confidence, we have employed these three models to analyze exemplary data on various compounds from different projects and targets. The presented framework allows researchers to tailor their experimental work and to arrive at a better understanding of their results, ultimately leading to more successful PROTAC discovery. While the focus here lies on in vitro pharmacology experiments, key implications for in vivo studies are also discussed.

Utilization of Physiologically Based Pharmacokinetic Modeling in Pharmacokinetic Study of Natural Medicine: An Overview

Natural medicine has been widely used for clinical treatment and health care in many countries and regions. Additionally, extracting active ingredients from traditional Chinese medicine and other natural plants, defining their chemical structure and pharmacological effects, and screening potential druggable candidates are also uprising directions in new drug research and development. Physiologically based pharmacokinetic (PBPK) modeling is a mathematical modeling technique that simulates the absorption, distribution, metabolism, and elimination of drugs in various tissues and organs in vivo based on physiological and anatomical characteristics and physicochemical properties. PBPK modeling in drug research and development has gradually been recognized by regulatory authorities in recent years, including the U.S. Food and Drug Administration. This review summarizes the general situation and shortcomings of the current research on the pharmacokinetics of natural medicine and introduces the concept and the advantages of the PBPK model in the study of pharmacokinetics of natural medicine. Finally, the pharmacokinetic studies of natural medicine using the PBPK models are summed up, followed by discussions on the applications of PBPK modeling to the enzyme-mediated pharmacokinetic changes, special populations, new drug research and development, and new indication adding for natural medicine. This paper aims to provide a novel strategy for the preclinical research and clinical use of natural medicine.

Advancing drug safety science by integrating molecular knowledge with post-marketing adverse event reports

Promising drug development efforts may frequently fail due to unintended adverse reactions. Several methods have been developed to analyze such data, aiming to improve pharmacovigilance and drug safety. In this work, we provide a brief review of key directions to quantitatively analyzing adverse events and explore the potential of augmenting these methods using additional molecular data descriptors. We argue that molecular expansion of adverse event data may provide a path to improving the insights gained through more traditional pharmacovigilance approaches. Examples include the ability to assess statistical relevance with respect to underlying biomolecular mechanisms, the ability to generate plausible causative hypotheses and/or confirmation where possible, the ability to computationally study potential clinical trial designs and/or results, as well as the further provision of advanced features incorporated in innovative methods, such as machine learning. In summary, molecular data expansion provides an elegant way to extend mechanistic modeling, systems pharmacology, and patient‐centered approaches for the assessment of drug safety. We anticipate that such advances in real‐world data informatics and outcome analytics will help to better inform public health, via the improved ability to prospectively understand and predict various types of drug‐induced molecular perturbations and adverse events.

HOXA7 promotes the metastasis of KRAS mutant colorectal cancer by regulating myeloid-derived suppressor cells

Background

KRAS mutation accounts for 30–50% of human colorectal cancer (CRC) cases. Due to the scarcity of effective treatment options, KRAS mutant CRC is difficult to treat in the clinic. Metastasis is still the major cause of the high mortality associated with KRAS mutant CRC, but the exact mechanism remains unclear. Here, we report a unique function of Homeobox 7 (HOXA7) in driving KRAS mutant CRC metastasis and explore therapeutic strategies for subpopulations of patients with this disease.

Methods

The expression of HOXA7 in a human CRC cohort was measured by immunohistochemistry. The function of HOXA7 in KRAS mutant CRC metastasis was analyzed with the cecum orthotopic model.

Results

Elevated HOXA7 expression was positively correlated with lymph node metastasis, distant metastasis, poor tumor differentiation, high TNM stage, and poor prognosis in CRC patients. Furthermore, HOXA7 was an independent prognostic marker in KRAS mutant CRC patients (P < 0.001) but not in KRAS wild-type CRC patients (P = 0.575). Overexpression of HOXA7 improved the ability of KRAS mutant CT26 cells to metastasize and simultaneously promoted the infiltration of myeloid-derived suppressor cells (MDSCs). When MDSC infiltration was blocked by a CXCR2 inhibitor, the metastasis rate of CT26 cells was markedly suppressed. The combination of the CXCR2 inhibitor SB265610 and programmed death-ligand 1 antibody (anti-PD-L1) could largely inhibit the metastasis of KRAS mutant CRC.

Conclusions

HOXA7 overexpression upregulated CXCL1 expression, which promoted MDSC infiltration. Interruption of this loop might provide a promising treatment strategy for HOXA7-mediated KRAS mutant CRC metastasis.