Comparative quantitative systems pharmacology modeling of anti-PCSK9 therapeutic modalities in hypercholesterolemia

Credibility assessment of a mechanistic model of atherosclerosis to predict cardiovascular outcomes under lipid-lowering therapy

Demonstrating cardiovascular (CV) benefits with lipid-lowering therapy (LLT) requires long-term randomized clinical trials (RCTs) with thousands of patients. Innovative approaches such as in silico trials applying a disease computational model to virtual patients receiving multiple treatments offer a complementary approach to rapidly generate comparative effectiveness data. A mechanistic computational model of atherosclerotic cardiovascular disease (ASCVD) was built from knowledge, describing lipoprotein homeostasis, LLT effects, and the progression of atherosclerotic plaques leading to myocardial infarction, ischemic stroke, major acute limb event and CV death. The ASCVD model was successfully calibrated and validated, and reproduced LLT effects observed in selected RCTs (ORION-10 and FOURIER for calibration; ORION-11, ODYSSEY-OUTCOMES and FOURIER-OLE for validation) on lipoproteins and ASCVD event incidence at both population and subgroup levels. This enables the future use of the model to conduct the SIRIUS programme, which intends to predict CV event reduction with inclisiran, an siRNA targeting hepatic PCSK9 mRNA.

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.

A case-study of model-informed drug development of a novel PCSK9 anti sense oligonucleotide. Part 1: First time in man to phase II

Here, we show model-informed drug development (MIDD) of a novel antisense oligonucleotide, targeting PCSK9 for treatment of hypocholesteremia. The case study exemplifies use of MIDD to analyze emerging data from an ongoing first-in-human study, utility of the US Food and Drug Administration MIDD pilot program to accelerate timelines, innovative use of competitor data to set biomarker targets, and use of MIDD to optimize sample size and dose selection, as well as to accelerate and de-risk a phase IIb study. The focus of the case-study is on the cross-functional collaboration and other key MIDD enablers that are critical to maximize the value of MIDD, rather than the technical application of MIDD.

Recent Advances in Gene Therapy for Familial Hypercholesterolemia: An Update Review

(1) Background: Existing lipid-lowering therapies have difficulty in achieving lipid target levels in patients with familial hypercholesterolemia (FH), especially in the treatment of patients with homozygous familial hypercholesterolemia. (2) Method: All of the literature data containing "Familial hypercholesterolemia" and "Gene Therapy" in PubMed and Clinical Trials from 2018 to 2022 were selected. (3) Results: The rapid development of gene therapy technology in recent years is expected to change the treatment status of FH patients. As emerging gene therapy vectors, the optimized adeno-associated viruses, exosomes, and lipid nanoparticles have demonstrated an improved safety and higher transfection efficiency. Various RNA-targeted therapies are in phase 1-3 clinical trials, such as small interfering RNA-based drugs inclisiran, ARO-ANG3, ARO-APOC3, olpasiran, SLN360, and antisense oligonucleotide-based drugs AZD8233, vupanorsen, volanesorsen, IONIS-APO(a)Rx, etc., all of which have demonstrated excellent lipid-lowering effects. With gene editing technologies, such as CRISPR-Cas 9 and meganuclease, completing animal experiments in mice or cynomolgus monkeys and demonstrating lasting lipid-lowering effects, patients with FH are expected to reach a permanent cure in the future. (4) Conclusion: Gene therapy is being widely used for the lipid-lowering treatment of FH patients and has shown excellent therapeutic promise, but the current delivery efficiency, economic burden, immunogenicity and the precision of gene therapy can be further optimized.

A mathematical model of in vitro hepatocellular cholesterol and lipoprotein metabolism for hyperlipidemia therapy

Cardiovascular diseases associated with high cholesterol (hypercholesterolemia) and low-density lipoproteins (LDL) levels are significant contributors to total mortality in developing and developed countries. Mathematical modeling of LDL metabolism is an important step in the development of drugs for hypercholesterolemia. The aim of this work was to develop and to analyze an integrated mathematical model of cholesterol metabolism in liver cells and its interaction with two types of drugs, statins and PCSK9 inhibitors. The model consisted of 21 ordinary differential equations (ODE) describing cholesterol biosynthesis and lipoprotein endocytosis in liver cells in vitro. The model was tested for its ability to mimic known biochemical effects of familial hypercholesterolemia, statin therapy, and PCSK9 inhibitors. The model qualitatively reproduced the well-known biology of cholesterol regulation, which confirms its potential for minimizing cellular research in initial testing of new drugs for cardiology.

Opportunities and challenges of physiologically based pharmacokinetic modeling in drug delivery

Physiologically based pharmacokinetic (PBPK) modeling is an important in silico tool to bridge drug properties and in vivo PK behaviors during drug development. Over the recent decade, the PBPK method has been largely applied to drug delivery systems (DDS), including oral, inhaled, transdermal, ophthalmic, and complex injectable products. The related therapeutic agents have included small-molecule drugs, therapeutic proteins, nucleic acids, and even cells. Simulation results have provided important insights into PK behaviors of new dosage forms, which strongly support drug regulation. In this review, we comprehensively summarize recent progress in PBPK applications in drug delivery, which shows large opportunities for facilitating drug development. In addition, we discuss the challenges of applying this methodology from a practical viewpoint.

Mathematical Models for Cholesterol Metabolism and Transport

Cholesterol is an essential component of eukaryotic cellular membranes. It is also an important precursor for making other molecules needed by the body. Cholesterol homeostasis plays an essential role in human health. Having high cholesterol can increase the chances of getting heart disease. As a result of the risks associated with high cholesterol, it is imperative that studies are conducted to determine the best course of action to reduce whole body cholesterol levels. Mathematical models can provide direction on this. By examining existing models, the suitable reactions or processes for drug targeting to lower whole-body cholesterol can be determined. This paper examines existing models in the literature that, in total, cover most of the processes involving cholesterol metabolism and transport, including: the absorption of cholesterol in the intestine; the cholesterol biosynthesis in the liver; the storage and transport of cholesterol between the intestine, the liver, blood vessels, and peripheral cells. The findings presented in these models will be discussed for potential combination to form a comprehensive model of cholesterol within the entire body, which is then taken as an in-silico patient for identifying drug targets, screening drugs, and designing intervention strategies to regulate cholesterol levels in the human body.

Modeling cholesterol metabolism and atherosclerosis

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of morbidity and mortality among Western populations. Many risk factors have been identified for ASCVD; however, elevated low-density lipoprotein cholesterol (LDL-C) remains the gold standard. Cholesterol metabolism at the cellular and whole-body level is maintained by an array of interacting components. These regulatory mechanisms have complex behavior. Likewise, the mechanisms which underpin atherogenesis are nontrivial and multifaceted. To help overcome the challenge of investigating these processes mathematical modeling, which is a core constituent of the systems biology paradigm has played a pivotal role in deciphering their dynamics. In so doing models have revealed new insights about the key drivers of ASCVD. The aim of this review is fourfold; to provide an overview of cholesterol metabolism and atherosclerosis, to briefly introduce mathematical approaches used in this field, to critically discuss models of cholesterol metabolism and atherosclerosis, and to highlight areas where mathematical modeling could help to investigate in the future. This article is categorized under: Cardiovascular Diseases > Computational Models.

Recent applications of quantitative systems pharmacology and machine learning models across diseases

Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019-2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.

A quantitative systems pharmacology modeling platform for evaluating triglyceride profiles in patients with high triglycerides receiving evinacumab

A model to quantitatively characterize the effect of evinacumab, an investigational monoclonal antibody against angiopoietin‐like protein 3 (ANGPTL3) on lipid trafficking is needed. A quantitative systems pharmacology (QSP) approach was developed to predict the transient responses of different triglyceride (TG)‐rich lipoprotein particles in response to evinacumab administration. A previously published hepatic lipid model was modified to address specific queries relevant to the mechanism of evinacumab and its effect on lipid metabolism. Modifications included the addition of intermediate‐density lipoprotein and low‐density lipoprotein compartments to address the modulation of lipoprotein lipase (LPL) activity by evinacumab, ANGPTL3 biosynthesis and clearance, and a target‐mediated drug disposition model. A sensitivity analysis guided the creation of virtual patients (VPs). The drug‐free QSP model was found to agree well with clinical data published with the initial hepatic liver model over simulations ranging from 20 to 365 days in duration. The QSP model, including the interaction between LPL and ANGPTL3, was validated against clinical data for total evinacumab, total ANGPTL3, and TG concentrations as well as inhibition of apolipoprotein CIII. Free ANGPTL3 concentration and LPL activity were also modeled. In total, seven VPs were created; the lipid levels of the VPs were found to match the range of responses observed in evinacumab clinical trial data. The QSP model results agreed with clinical data for various subjects and was shown to characterize known TG physiology and drug effects in a range of patient populations with varying levels of TGs, enabling hypothesis testing of evinacumab effects on lipid metabolism.

An oral antisense oligonucleotide for PCSK9 inhibition

Inhibitors of proprotein convertase subtilisin/kexin type 9 (PCSK9) reduce low-density lipoprotein (LDL) cholesterol and are used for treatment of dyslipidemia. Current PCSK9 inhibitors are administered via subcutaneous injection. We present a highly potent, chemically modified PCSK9 antisense oligonucleotide (ASO) with potential for oral delivery. Past attempts at oral delivery using earlier-generation ASO chemistries and transient permeation enhancers provided encouraging data, suggesting that improving potency of the ASO could make oral delivery a reality. The constrained ethyl chemistry and liver targeting enabled by N-acetylgalactosamine conjugation make this ASO highly potent. A single subcutaneous dose of 90 mg reduced PCSK9 by >90% in humans with elevated LDL cholesterol and a monthly subcutaneous dose of around 25 mg is predicted to reduce PCSK9 by 80% at steady state. To investigate the feasibility of oral administration, the ASO was coformulated in a tablet with sodium caprate as permeation enhancer. Repeated oral daily dosing in dogs resulted in a bioavailability of 7% in the liver (target organ), about fivefold greater than the plasma bioavailability. Target engagement after oral administration was confirmed by intrajejunal administration of a rat-specific surrogate ASO in solution with the enhancer to rats and by plasma PCSK9 and LDL cholesterol lowering in cynomolgus monkey after tablet administration. On the basis of an assumption of 5% liver bioavailability after oral administration in humans, a daily dose of 15 mg is predicted to reduce circulating PCSK9 by 80% at steady state, supporting the development of the compound for oral administration to treat dyslipidemia.

Comparisons of basic target-mediated drug disposition (TMDD) and ligand facilitated target removal (LFTR)

In the well-known model for basic Target-Mediated Drug Disposition (TMDD), drug binds to the target and the resulting drug-target complex is removed by a first order process, leading to loss of both drug and target. In the present note we study what happens when, instead, drug is returned to the free drug pool so that it can a new target molecule. What results is a mechanism in which the drug, here referred to as the ligand, facilitates the removal of the target,and then returns to the free ligand pool. Accordingly the process will be referred to as Ligand-Facilitated Target Removal (LFTR). It is shown through simulations and mathematical analysis how the two models differ and how their signature profiles typically appear. We also derive a useful parameter of both models, the in vivo potency EC₅₀ (L₅₀) which contains both ligand-target binding properties (k_on,k_off), target turnover (k_deg) and ligand-target complex kinetics (k_e(RL)). Thus, this parameter contains a conglomerate of properties and is therefore potentially more informative about relevant (clinical) exposure than the binding affinity (K_d) alone. The derived potency parameter EC₅₀ may therefore be used as a more robust ranking parameter among small and large drug molecules in drug discovery. Subsequently the LFTR model is applied to experimentally obtained literature data and the relevant parameters are estimated.

Proprotein convertase subtilisin/kexin type 9 inhibition as the next statin?

PURPOSE OF REVIEW

Despite the wide use of statins and other LDL-cholesterol (LDL-C)-lowering therapies, atherosclerotic cardiovascular disease remains an important cause of mortality and morbidity. Here, we discuss efficacy, side effects and convenience of current and future therapies inhibiting proprotein convertase subtilisin/kexin type 9 (PCSK9).

RECENT FINDINGS

Clinical trials with mAbs administered every 2-4 weeks and small interfering RNAs given two to four times per year have consistently demonstrated substantial LDL-C-lowering (40-60%) and improved outcome when added to existing lipid-lowering therapies. Pleiotropic effects of PCSK9 inhibition are somewhat different from those observed with statin treatment as evidenced by reduced levels of triglycerides and lipoprotein(a) with no apparent effect on inflammatory markers in patients treated with PCSK9 inhibitors. Treatment with mAb and small interfering RNA are associated with a high-cost, however, small molecules and vaccines may improve cost and convenience if development of these are successful.

SUMMARY

PCSK9 inhibitors are currently considered to be an add-on therapy and whether these drugs will be used as stand-alone and/or as a first choice is dependent on clinical readouts from ongoing and future trials, real-world evidence, convenience and treatment costs.