The genomic mosaic of mitochondrial dysfunction: Decoding nuclear and mitochondrial epigenetic contributions to maternally inherited diabetes and deafness pathogenesis.

Aims: Maternally inherited diabetes and deafness (MIDD) is a complex disorder characterized by multiorgan clinical manifestations, including diabetes, hearing loss, and ophthalmic complications. This pilot study aimed to elucidate the intricate interplay between nuclear and mitochondrial genetics, epigenetic modifications, and their potential implications in the pathogenesis of MIDD. Main methods: A comprehensive genomic approach was employed to analyze a Sicilian family affected by clinically characterized MIDD, negative to the only known causative m.3243 A > G variant, integrating whole-exome sequencing and whole-genome bisulfite sequencing of both nuclear and mitochondrial analyses. Key findings: Rare and deleterious variants were identified across multiple nuclear genes involved in retinal homeostasis, mitochondrial function, and epigenetic regulation, while complementary mitochondrial DNA analysis revealed a rich tapestry of genetic diversity across genes encoding components of the electron transport chain and ATP synthesis machinery. Epigenetic analyses uncovered significant differentially methylated regions across the genome and within the mitochondrial genome, suggesting a nuanced landscape of epigenetic modulation. Significance: The integration of genetic and epigenetic data highlighted the potential crosstalk between nuclear and mitochondrial regulation, with specific mtDNA variants influencing methylation patterns and potentially impacting the expression and regulation of mitochondrial genes. This pilot study provides valuable insights into the complex molecular mechanisms underlying MIDD, emphasizing the interplay between nucleus and mitochondrion, tracing the way for future research into targeted therapeutic interventions and personalized approaches for disease management.

Model-informed drug development in pediatric, pregnancy and geriatric drug development: States of the art and future.

The challenges of drug development in pediatric, pregnant and geriatric populations are a worldwide concern shared by regulatory authorities, pharmaceutical companies, and healthcare professionals. Model-informed drug development (MIDD) can integrate and quantify real-world data of physiology, pharmacology, and disease processes by using modeling and simulation techniques to facilitate decision-making in drug development. In this article, we reviewed current MIDD policy updates, reflected on the integrity of physiological data used for MIDD and the effects of physiological changes on the drug PK, as well as summarized current MIDD strategies and applications, so as to present the state of the art of MIDD in pediatric, pregnant and geriatric populations. Some considerations are put forth for the future improvements of MIDD including refining regulatory considerations, improving the integrity of physiological data, applying the emerging technologies, and exploring the application of MIDD in new therapies like gene therapies for special populations.

Application of quantitative protein mass spectrometric data in the early predictive analysis of membrane-bound target engagement by monoclonal antibodies.

Model-informed drug discovery advocates the use of mathematical modeling and simulation for improved efficacy in drug discovery. In the case of monoclonal antibodies (mAbs) against cell membrane antigens, this requires quantitative insight into the target tissue concentration levels. Protein mass spectrometry data are often available but the values are expressed in relative, rather than in molar concentration units that are easier to incorporate into pharmacokinetic models. Here, we present an empirical correlation that converts the parts per million (ppm) concentrations in the PaxDb database to their molar equivalents that are more suitable for pharmacokinetic modeling. We evaluate the insight afforded to target tissue distribution by analyzing the likely tumor-targeting accuracy of mAbs recognizing either epidermal growth factor receptor or its homolog HER2. Surprisingly, the predicted tissue concentrations of both these targets exceed the Kd values of their respective therapeutic mAbs. Physiologically based pharmacokinetic (PBPK) modeling indicates that in these conditions only about 0.05% of the dosed mAb is likely to reach the solid tumor target cells. The rest of the dose is eliminated in healthy tissues via both nonspecific and target-mediated processes. The presented approach allows evaluation of the interplay between the target expression level in different tissues that determines the overall pharmacokinetic properties of the drug and the fraction that reaches the cells of interest. This methodology can help to evaluate the efficacy and safety properties of novel drugs, especially if the off-target cell degradation has cytotoxic outcomes, as in the case of antibody-drug conjugates.

A Minimal PBPK/PD Model with Expansion-Enhanced Target-Mediated Drug Disposition to Support a First-in-Human Clinical Study Design for a FLT3L-Fc Molecule.

FLT3L-Fc is a half-life extended, effectorless Fc-fusion of the native human FLT3-ligand. In cynomolgus monkeys, treatment with FLT3L-Fc leads to a complex pharmacokinetic/pharmacodynamic (PK/PD) relationship, with observed nonlinear PK and expansion of different immune cell types across different dose levels. A minimal physiologically based PK/PD model with expansion-enhanced target-mediated drug disposition (TMDD) was developed to integrate the molecule's mechanism of action, as well as the complex preclinical and clinical PK/PD data, to support the preclinical-to-clinical translation of FLT3L-Fc. In addition to the preclinical PK data of FLT3L-Fc in cynomolgus monkeys, clinical PK and PD data from other FLT3-agonist molecules (GS-3583 and CDX-301) were used to inform the model and project the expansion profiles of conventional DC1s (cDC1s) and total DCs in peripheral blood. This work constitutes an essential part of our model-informed drug development (MIDD) strategy for clinical development of FLT3L-Fc by projecting PK/PD in healthy volunteers, determining the first-in-human (FIH) dose, and informing the efficacious dose in clinical settings. Model-generated results were incorporated in regulatory filings to support the rationale for the FIH dose selection.

Physiologically Based Biopharmaceutics Modeling (PBBM): Best Practices for Drug Product Quality, Regulatory and Industry Perspectives: 2023 Workshop Summary Report.

Physiologically based biopharmaceutics modeling (PBBM) is used to elevate drug product quality by providing a more accurate and holistic understanding of how drugs interact with the human body. These models are based on the integration of physiological, pharmacological, and pharmaceutical data to simulate and predict drug behavior in vivo. Effective utilization of PBBM requires a consistent approach to model development, verification, validation, and application. Currently, only one country has a draft guidance document for PBBM, whereas other major regulatory authorities have had limited experience with the review of PBBM. To address this gap, industry submitted confidential PBBM case studies to be reviewed by the regulatory agencies; software companies committed to training. PBBM cases were independently and collaboratively discussed by regulators, and academic colleagues participated in some of the discussions. Successful bioequivalence "safe space" industry case examples are also presented. Overall, six regulatory agencies were involved in the case study exercises, including ANVISA, FDA, Health Canada, MHRA, PMDA, and EMA (experts from Belgium, Germany, Norway, Portugal, Spain, and Sweden), and we believe this is the first time such a collaboration has taken place. The outcomes were presented at this workshop, together with a participant survey on the utility and experience with PBBM submissions, to discuss the best scientific practices for developing, validating, and applying PBBMs. The PBBM case studies enabled industry to receive constructive feedback from global regulators and highlighted clear direction for future PBBM submissions for regulatory consideration.

An industry perspective on current QSP trends in drug development.

2023 marks the 10th anniversary of Natpara's submission to the US FDA, which led to the first recorded regulatory interaction where a decision was supported by Quantitative and Systems Pharmacology (QSP) simulations. It had taken about 5 years for the timid QSP discipline to emerge as an effective Model-Informed Drug Development (MIDD) tool with visible impact in the pharmaceutical industry. Since then, the presence of QSP in the regulatory environment has continued to increase, to the point that the Agency reported 60 QSP submissions in 2020 alone, representing ~ 4% of their annual IND submissions [1]. What sort of industry mindset has enabled QSP to reach this level of success? How does QSP fit within the MIDD paradigm? Does QSP mean the same to Discovery and to Clinical Development projects? How do 'platforms' compare to 'fit-for-purpose' QSP models in an industrial setting? Can QSP and empirical Pharmacokinetic-Pharmacodynamic (PKPD) modelling be complementary? What level of validation is required to inform drug development decisions? This article reflects on all these questions, in particular addressing those audiences with limited line-of-sight into the drug industry decision-making machinery.

T cell activation contributes to purifying selection against the MELAS-associated m.3243A>G pathogenic variant in blood.

T cells have been shown to maintain a lower percentage (heteroplasmy) of the pathogenic m.3243A>G variant (MT-TL1, associated with maternally inherited diabetes and deafness [MIDD] and mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes [MELAS]). The mechanism(s) underlying this purifying selection, however, remain unknown. Here we report that purified patient memory CD4+ T cells have lower bulk m.3243A>G heteroplasmy compared to naïve CD4+ T cells. In vitro activation of naïve CD4+ m.3243A>G patient T cells results in lower bulk m.3243A>G heteroplasmy after proliferation. Finally, m.3243A>G patient T cell receptor repertoire sequencing reveals relative oligoclonality compared to controls. These data support a role for T cell activation in peripheral, purifying selection against high m.3243A>G heteroplasmy T cells at the level of the cell, in a likely cell-autonomous fashion.

Application of Model-Informed Drug Development in Dose Selection and Optimization for siRNA Therapies.

The application of model-informed drug development (MIDD) has revolutionized drug development and regulatory decision making, transforming the process into one that is more efficient, effective, and patient centered. A critical application of MIDD is to facilitate dose selection and optimization, which play a pivotal role in improving efficacy, safety, and tolerability profiles of a candidate drug. With the surge of interest in small interfering RNA (siRNA) drugs as a promising class of therapeutics, their applications in various disease areas have been extensively studied preclinically. However, dosing selection and optimization experience for siRNA in humans is limited. Unique challenges exist for the dose evaluation of siRNA due to the temporal discordance between pharmacokinetic and pharmacodynamic profiles, as well as limited available clinical experience and considerable interindividual variability. This review highlights the pivotal role of MIDD in facilitating dose selection and optimization for siRNA therapeutics. Based on past experiences with approved siRNA products, MIDD has demonstrated its ability to aid in dose selection for clinical trials and enabling optimal dosing for the general patient population. In addition, MIDD presents an opportunity for dose individualization based on patient characteristics, enhancing the precision and effectiveness of siRNA therapeutics. In conclusion, the integration of MIDD offers substantial advantages in navigating the complex challenges of dose selection and optimization in siRNA drug development, which in turn accelerates the development process, supports regulatory decision making, and ultimately improves the clinical outcomes of siRNA-based therapies, fostering advancements in precision medicine across a diverse range of diseases.

The clinical and genetic characteristics of maternally inherited diabetes and deafness (MIDD) with mitochondrial m.3243A > G mutation: A 10-year follow-up observation study and literature review.

Maternally inherited diabetes and deafness (MIDD) is often caused by the m.3243A > G mutation in mitochondrial DNA. Unfortunately, the characteristics of MIDD, especially long-term outcomes and heteroplasmic changes, have not been well described previously. The purpose of this study was to describe the clinical and genetic features of a family with MIDD after 10 years of follow-up.A 33-year-old male patient with typical characteristics of MIDD, including early-onset diabetes, deafness, and low body mass index, was admitted to our department. Further investigation revealed that the vast majority of his maternal relatives suffered from diabetes with or without deafness. A detailed family history was then requested from the patient and a pedigree was constructed. The patient suspected of MIDD was screened for mutations using whole mitochondrial DNA sequencing. Candidate pathogenic variants were then validated in other family members through Sanger sequencing. The patient was diagnosed with MIDD, with inherited m.3243A > G mutation in the mitochondrially encoded tRNA leucine 1 (MT-TL1) gene, after 10 years of symptom onset. The patient was then treated with insulin and coenzyme Q10 to improve mitochondrial function. During the follow-up period, his fasting blood glucose and HbA1c levels were improved and the incidence of diabetic ketoacidosis was significantly reduced. Our findings indicate that whole mitochondrial DNA sequencing should be considered for patients suspected of MIDD to improve the efficiency of diagnosis and prognosis.

Population Pharmacokinetic and Pharmacodynamic Modeling of Romiplostim Biosimilar GP40141 and Reference Product in Healthy Volunteers to Evaluate Biosimilarity.

GP40141 is a romiplostim biosimilar. A Phase 1 clinical trial was previously conducted in healthy volunteers to evaluate the pharmacodynamics (PD), pharmacokinetics (PK), and safety of GP40141 compared to the reference romiplostim (NCT05652595). Using noncompartmental analysis, the biosimilarity of PD end points was determined according to the classical criterion (0.8-1.25). PK end points were also in good agreement between GP40141 and the reference romiplostim; however, the confidence interval for the area under concentration-time curve from time 0 to the time of last measurement was slightly out of the bioequivalence range (0.91-1.29). Population PK/PD was used in the present study to characterize the individual PK and PD data of 56 healthy subjects in 2 cross-over periods of the Phase 1 clinical trial. Body weight and neutralizing antibodies to romiplostim were found to be important predictors of apparent volume of distribution and linear elimination constant, respectively. Within the framework of the conducted modeling, population estimates of PK/PD parameters were obtained, which were in agreement with literature data for the reference romiplostim. Additionally, values of intersubject variability, previously unreported for romiplostim in a healthy subject population, were derived. Covariate analysis, conducted during model development, as well as visual diagnostics and model-based simulations, demonstrated the absence of significant differences in PK and PD between GP40141 and romiplostim-ref.

Maternally Inherited Diabetes and Deafness (MIDD) - Atypical Clinical Diabetes Features Leading to the Diagnosis.

Maternally inherited diabetes and deafness (MIDD) syndrome refers to a rarely diagnosed disorder caused by pathogenic variants in mtDNA. It was first identified in 1992 and, to date, is considered underdiagnosed because of misclassification to type 1 or type 2 diabetes mellitus. MIDD reflects a multisystem metabolic syndrome commonly resulting in insulin-requiring diabetes and sensorineural deafness but can also lead to a broad range of other manifestations. The spectrum of pathology differs among individuals, likely because of varied degrees of heteroplasmy associated with mtDNA. Heteroplasmy also creates diagnostic difficulties, with a high index of suspicion required to diagnose MIDD in some cases. Here, we review a patient with MIDD who presented with an atypical clinical diabetes picture, additionally documenting his pedigree. To our knowledge, this is the first Cypriot reported with MIDD.

Rare oncology therapeutics: review of clinical pharmacology package of drug approvals (2019-2023) by US FDA, best practices and recommendations.

There are many challenges with rare diseases drug development and rare oncology indications are not different. To understand the regulatory landscape as it relates to application of clinical pharmacology principles in rare oncology product development, we reviewed publicly available information of 39 approvals by US FDA between January 2019 and March 2023. The objective was to understand the expected clinical pharmacology studies and knowledge base in such approvals. Model informed drug development (MIDD) applications were also reviewed, as such approaches are expected to play a critical role in filling clinical pharmacology gaps in rare oncology, where number of clinical trials and size of these trials will perhaps continue to be small. The findings highlighted how clinical pharmacology contributed to the evidence of effectiveness, dose optimization and elucidation of intrinsic and extrinsic factors affecting drug's behavior. Clinical pharmacology studies were often integrated with modeling in many of the NDAs/BLAs. Of the post marketing requirements (PMR) received, 18% were for dose optimization, 49% for DDI, 8% for QTc, 49% for specific population, and 5% for food effect. Two post marketing commitments (PMC) were issued for immunogenicity of the 11 biologics submissions. 15% (6 of 39) of the submissions used maximum tolerated dose (MTD) to advance their molecule into Phase 2 studies. Of them 3 approvals received PMR for dose optimization. 3 + 3 was the most prevalent Phase 1 design with use in 74% of the New Drug Applications (NDA)/Biologic License Applications (BLA) reviewed. Rest used innovative approaches such as BLRM, BOIN or mTPi, with BLRM being the most common. Seamless clinical pharmacology and MIDD approaches are paramount for rare oncology drug development.

Is There a Need for a Dedicated Pharmacokinetic Trial for a Drug in Obese Populations? A Drug Prioritization Decision Tree Framework.

Obesity is a growing global health concern associated with high comorbidity rates, leading to an increasing number of patients who are obese requiring medication. However, clinical trials often exclude or under-represent individuals who are obese, creating the need for a methodology to adjust labeling to ensure safe and effective dosing for all patients. To address this, we developed a 2-part decision tree framework to prioritize drugs for dedicated pharmacokinetic studies in obese subjects. Leveraging current drug knowledge and modeling techniques, the decision tree system predicts expected exposure changes and recommends labeling strategies, allowing stakeholders to prioritize resources toward the drugs most in need. In a case study evaluating 30 drugs from literature across different therapeutic areas, our first decision tree predicted the expected direction of exposure change accurately in 73% of cases. We conclude that this decision tree system offers a valuable tool to advance research in obesity pharmacology and personalize drug development for patients who are obese, ensuring safe and effective medication.

Role of Modeling and Simulation in Preclinical and Clinical Long-Acting Injectable Drug Development.

Innovations in the field of long-acting injectable drug development are increasingly being reported. More advanced in vitro and in vivo characterization can improve our understanding of the injection space and aid in describing the long-acting injectable (LAI) drug's behavior at the injection site more mechanistically. These innovations may enable unlocking the potential of employing a model-based framework in the LAI preclinical and clinical space. This review provides a brief overview of the LAI development process before delving deeper into the current status of modeling and simulation approaches in characterizing the preclinical and clinical LAI pharmacokinetics, focused on aqueous crystalline suspensions. A closer look is provided on in vitro release methods, available biopharmaceutical models and reported in vitro/in vivo correlations (IVIVCs) that may advance LAI drug development. The overview allows identifying the opportunities for use of model-informed drug development approaches and potential gaps where further research may be most warranted. Continued investment in improving our understanding of LAI PK across species through translational approaches may facilitate the future development of LAI drug products.

Evaluation of the non-linearity of NA808 in liver not reflected in plasma using a rat pharmacokinetic study and PBPK modelling.

When NA808, a potent HCV replication inhibitor, was intravenously administered to rats, it was distributed to the liver. The AUC ratio in the liver of 20 mg/kg to 2 mg/kg was greater than the dose ratio, whereas exposure in plasma was increased in a dose-proportional manner. Saturation of biliary excretion was also shown at 20 mg/kg.NA808 was revealed to be a substrate for both OATP1B and MRP2 transporters by an in vitro study using OATP1B1-MRP2 expressing cells. [14C]NA808 was taken up into the cells by OATP1B1 and excreted from cells by MRP2 efficiently (Papp ratio: 24.2-70.2). The Papp ratio decreased with increasing NA808 concentration.PBPK modelling was constructed to display the blood and liver concentration time profile and biliary excretion of NA808. This model analysis was able to reproduce the pharmacokinetics in rats; the degree of increase in the liver exposure from 2 to 20 mg/kg was more than dose-proportional and was greater than the increase in the blood exposure due to saturation of efflux transporters.In drug development, to avoid unexpected toxicity in tissues, it is important to consider the potential for tissue non-linearity with linear plasma exposure based on pre-clinical data and PBPK modelling.

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.

Mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes syndrome: a case report from Nepal.

Mitochondrial encephalomyopathy, lactic acidosis, and stroke-like syndrome (MELAS) is a rare neurodegenerative inherited disorder that is characterized by stroke-like episodes, seizures, endocrine, and multiple system involvement. It is important to consider it as a differential diagnosis in a young patient with stroke-like episodes as it is progressive and has multiple complications. Case presentation: A 28-year-old male presented with slurring of speech and drowsiness for 7 h. He was a diagnosed case of type 2 diabetes mellitus, Wolf-Parkinson-White syndrome, and bilateral hearing loss. Clinical findings and investigations: The patient had expressive aphasia with impaired fluency, repetition, and naming. After being discharged, he represented with loss of consciousness and involuntary movements of the whole body. MRI and MRS showed extension of hyperintense lesions to parieto-occipital regions from temporal regions not limited by vascular territories. MELAS was considered, which was confirmed by molecular genetic analysis. Coenzyme Q10 was used for MELAS. Insulin, Linagliptin, and levetiracetam were used for diabetes and seizures. Regular follow-up was advised to the patient.MELAS is an important syndrome to consider in any young patient presenting with unexplained stroke disorders. A high index of suspicion is needed in an appropriate clinical setting to avoid misdiagnosis.

Coming full circle: The potential utility of real-world evidence to discern predictions from a physiologically based pharmacokinetic model.

Today real word data (RWD) are playing a greater role in informing health care decisions. A physiologically based pharmacokinetic model (PBPK) and observed exposure-risk relationship predicted an increased bleeding risk induced by rivaroxaban (RXB) in patients with mild to moderate chronic kidney disease (CKD) taking concomitant medications that are combined Pgp-CYP3A inhibitors. In this commentary, we explore the potential use of RWD to assess the clinical consequence of this complex drug-drug interaction predicted from PBPK. This is a retrospective, case control, pilot study using a RWD dataset of 896,728 patients with mild to moderate chronic kidney disease and rivaroxaban use that was refined based upon combined Pgp-CYP3A inhibitor exposure and report of drug-induced bleeding (DIB). The odds ratio of patients with mild to moderate chronic kidney disease taking rivaroxaban with or without concurrent Pgp-CYP3A inhibitor use having a DIB was calculated. The odds ratio for DIB was 2.04 (CI95 1.82, 2.3; p < 0.001) suggesting an approximate doubling of bleeding risk which is consistent with the rivaroxaban exposure changes predicted by the published PBPK model and observed exposure-risk relationship. This exploratory analysis demonstrated the potential utility of RWD to assess model-based predictions as part of a drugs life cycle management.