This review examines how model-informed drug development, particularly pharmacometric modeling, has emerged as an essential tool in the advancement of siRNA therapeutics and how quantitative pharmacology addresses developmental challenges, including first-in-human dose selection, optimal dosing intervals, organ impairment effects, and interindividual variability.
Abstract
Small interfering RNA (siRNA) therapeutics represent a paradigm shift in targeting previously undruggable diseases through specific gene silencing. Since the FDA's first approval of patisiran in 2018, the field has expanded rapidly, with eight approved drugs and numerous clinical candidates. However, the atypical pharmacokinetic-pharmacodynamic (PK-PD) profile of siRNAs, characterized by rapid plasma elimination, prolonged tissue retention, and temporal dissociation between exposure and effect, has created distinct obstacles for conventional dose-finding strategies. This review examines how model-informed drug development, particularly pharmacometric modeling, has emerged as an essential tool in the advancement of siRNA therapeutics. Population PK-PD models for all FDA-approved siRNA drugs are systematically described, demonstrating that mechanistic and semi-mechanistic approaches inform preclinical-to-clinical translation, optimize dosing regimens, and support regulatory decisions. Key modeling frameworks include: (1) mechanistic models incorporating asialoglycoprotein receptor (ASGPR)-mediated uptake, RNA-induced silencing complex (RISC) loading, and mRNA degradation kinetics; (2) minimal physiologically-based PK-PD models for interspecies scaling; and (3) exposure-response models addressing the disconnect between plasma PK and pharmacological activity. This review also discusses how quantitative pharmacology addresses developmental challenges, including first-in-human dose selection, optimal dosing intervals, organ impairment effects, and interindividual variability. This pharmacometric perspective provides a quantitative framework for rational development of safe and effective siRNA therapeutics across diverse patient populations.
This review summarizes current and emerging model‐informed drug development applications in oligonucleotide therapeutics, with primary emphasis on siRNAs and complementary insights from ASOs.
Paridhi Gupta, Mindy Magee, Vivaswath S. Ayyar· Journal of clinical pharmaco...· 0 citations
Nanomedicine has transformed therapeutic strategies by enabling precise delivery of nucleic acid-based drugs, including small interfering RNA (siRNA), messenger RNA (mRNA), and antisense oligonucleotides. A landmark achievement is Onpattro (patisiran), the first FDA-approved RNAi therapy, which employs lipid nanoparticles (LNPs) to silence transthyretin in hereditary amyloidosis. Its approval validates RNAi as a viable therapeutic modality and underscores the central role of nanocarriers in clinical translation. Despite this success, barriers such as nanoparticle stability, targeted delivery, immunogenicity, and manufacturing scalability remain. Recent advances in mRNA vaccines, CRISPR-based gene editing, and stimuli-responsive nanoparticles are addressing these challenges, supported by growing clinical case studies and real-world data. This review highlights Onpattro’s clinical development, compares delivery platforms, discusses translational challenges, and examines emerging technologies that will guide the next generation of RNAi nanomedicines in personalized therapy.
Dilpreet Singh, Satvir Singh, Nitin Tandon et al.· Iranian Journal of Basic Med...· 1 citation
This review first provides a concise overview of the mechanistic principles underlying oligonucleotide function and commonly employed chemical modification techniques, and highlights recent advancements in receptor-mediated delivery systems for extrahepatic targeting, and dual-targeting oligonucleotide engagement strategies.
Liuhai Chen, Jiahao Xu, Jin Li et al.· The Innovation Drug Discover...· 2 citations
Cervical cancer pharmacotherapy is significantly limited by physiological and cellular barriers that restrict drug access to therapeutic targets, resulting in suboptimal biodistribution, systemic toxicity, and the emergence of drug resistance. This review provides a mechanistic and biopharmaceutics-centered analysis of how advanced drug delivery systems are being engineered to overcome these limitations. We critically examine the role of nanocarriers, including lipid-based vesicles, polymeric nanoparticles, and inorganic hybrid systems, in modulating absorption, distribution, and tumor-targeting efficiency, with emphasis on their physicochemical properties and interaction with biological barriers such as the tumor microenvironment and cellular uptake pathways. In parallel, we analyze nucleic acid-based therapeutics (CRISPR/Cas systems, miRNA, and antisense oligonucleotides) from a pharmaceutical sciences perspective, focusing on delivery constraints, stability, intracellular trafficking, and their ability to modulate pharmacological response and drug resistance mechanisms. The review also discusses the integration of immunomodulatory strategies within nanodelivery platforms as a means to alter disease-related biological barriers and improve therapeutic index. Finally, we explore the emerging role of AI-assisted models in optimizing formulation design, predicting pharmacokinetic behavior, and supporting precision dosing strategies in drug development workflows. By integrating drug delivery engineering, molecular biopharmaceutics, and computational optimization, this work outlines a translational framework for overcoming key barriers in pharmaceutical intervention design for oncology applications.
Manoj Dalabehera, Shubham K. Chaudhari, Jatin Kumar et al.· Journal of Pharmacy and Scie...· 0 citations
G-protein Coupled Receptors (GPCRs) are the largest family of classical membrane receptors and the most important class of pharmacological targets, playing roles in many physiological and pathological processes. Targeting a GPCR is a challenging approach because traditional drugs and similar therapeutics have a number of significant drawbacks, including low aqueous solubility, low bioavailability, rapid metabolic degradation, low tissue specificity, and off-target effects. This review discusses different nanocarrier-based drug delivery strategies to improve the therapeutic efficacy, targeting efficiency, and translational potential of drugs acting on GPCRs.
A systematic review of peer-reviewed published articles to explore the latest developments in GPCR biology, GPCR signalling pathways, and drug delivery using nanocarriers was conducted. Major nanoplatforms, such as liposomes, polymeric nanoparticles, dendrimers, and hybrid nanosystems, were studied based on their design principles, targeting strategies, and therapeutic applications. Preclinical, mechanistic, and early translational studies that were relevant were included.
Nanocarrier-based systems have multiple advantages in GPCR-targeted therapy, such as enhanced drug stability, increased bioavailability, controlled release, and reduced systemic toxicity. Functionalization of nanocarriers may improve delivery in a receptor-specific manner and facilitate transport across biological barriers, such as the blood-brain barrier. These systems also support multiple functions, including co-delivery of therapeutics, nucleic acids, and diagnostic components. Promising applications have been identified in oncology, neurological disorders, cardiovascular diseases, and inflammatory conditions. However, there are still challenges in translation, particularly with regard to immunogenicity, long-term safety, manufacturing scalability, and regulatory approval.
The combined use of GPCR pharmacology with state-of-the-art nanocarrier engineering is a promising approach to improve receptor selectivity and therapeutic precision. Novel instruments, such as artificial intelligence, molecular modelling, and systems pharmacology, may further aid the optimisation of ligand selection, carrier design, and personalised therapeutic development.
Nanocarrier-mediated drug delivery is a sensible and highly promising strategy to address the limitations of conventional GPCR therapeutics. In addition, developments in targeted nanomedicine may accelerate the development of accurate and individualised GPCR-targeted therapeutic treatments.
P. Wal, Jyotsana Dwivedi, K. Khairunnisa et al.· Current Drug Targets· 0 citations
Despite major advances in anticancer drug development, the successful translation of promising preclinical findings into effective clinical therapies remains a major challenge in oncology. Many drug candidates demonstrate strong efficacy in experimental models but ultimately fail during clinical development due to limited therapeutic benefit, unexpected toxicity, or poor reproducibility of preclinical outcomes in patients. Rather than attributing these failures solely to limitations of preclinical models, this thesis demonstrates that translational failures largely arise from the way models are designed, interpreted, and applied. Improving predictive performance therefore requires experimental strategies that more accurately reproduce the pharmacological and biological conditions encountered in patients. This research was based on the hypothesis that integrating clinically relevant pharmacokinetic-pharmacodynamic (PKPD) principles with biologically patient representative tumor models can improve translational predictability. The work first identifies key factors contributing to the disconnect between preclinical and clinical outcomes, including species-specific differences in drug disposition, pharmacological response, and conventional dosing strategies. Current approaches frequently rely on maximum tolerated dose (MTD) regimens in mice, resulting in drug exposures that exceed clinically achievable levels and may overestimate therapeutic efficacy. These findings support a pharmacological humanization framework in which patient-relevant drug exposure becomes the primary determinant of preclinical study design. Implementation of this strategy required the development of robust analytical methodologies. Two highly sensitive liquid chromatography–tandem mass spectrometry (LC–MS/MS) methods were developed and validated, enabling accurate quantification of ABT-751 and multiple pharmacologically relevant compounds across biological matrices. These analytical platforms supported comprehensive pharmacokinetic studies, exposure-response analyses, and more efficient experimental designs. Application of these methods demonstrated that the limited clinical efficacy of ABT-751 was not caused by insufficient systemic or intratumoral exposure. Instead, resistance was primarily associated with biological characteristics of the tumor microenvironment, particularly hypoxia. These findings highlight the importance of clinically relevant tumor models capable of identifying resistance mechanisms that remain undetected in conventional xenograft systems. Pharmacologically humanized mouse models were subsequently applied to investigate anticancer therapies under clinically relevant exposure conditions. Studies with trametinib and abemaciclib showed that patient-equivalent exposures produced less pronounced tumor responses than conventional MTD-based dosing but more accurately reflected clinical outcomes. These findings demonstrate that maximizing antitumor effects in mice does not necessarily improve clinical translation and that accurate exposure matching between species is essential for predictive preclinical research. The same principles were extended to central nervous system drug delivery. Investigation of the ATP-binding cassette transporters ABCB1 and ABCG2 demonstrated that effective inhibition of blood–brain barrier transport requires higher dual inhibitor exposures than previously achieved clinically. Furthermore, evaluation of approved JAK-STAT3 inhibitors for brain metastases revealed insufficient target inhibition at clinically relevant exposures, limiting their therapeutic potential. Overall, this thesis demonstrates that improving oncology drug development requires refinement of preclinical models through pharmacological humanization and biologically relevant experimental design. Integrating clinically representative drug exposure with translational tumor models provides a more reliable framework for predicting therapeutic efficacy, improving decision-making before clinical trials, and increasing the likelihood of successful translation of novel anticancer therapies.