Drug development is fraught with difficulties, and pharmaceutical technology is becoming increasingly complex. With the development of computer technology, the pharmaceutical industry is paying more and more attention to the model-informed drug development (MIDD) strategy in an attempt to solve the difficulties of drug development. MIDD is a rapidly developing field. However, how to apply modeling and simulation technology in the long-term, high investment, and high-risk work of drug development to improve research and development efficiency and avoid safety issues is a question worthy of careful study. In this regard, the attitude of regulatory authorities in the pharmaceutical industry cannot be ignored. This chapter summarizes the recent guidance principles and discussions on model application in the MIDD field by drug regulatory authorities in many countries, especially the US FDA. Overall, regulatory agencies are supportive and open to the development of MIDD, but they are still very cautious in the application of models.

The lipid nanoparticle (LNP) has been so far proven as a strongly effective delivery system for mRNA and siRNA. However, the mechanisms of LNP’s distribution, metabolism, and elimination are complicated, while the transportation and pharmacokinetics (PK) of LNP are just sparsely investigated and simply described. This study aimed to build a model for the transportation of RNA-LNP in Hela cells, rats, mice, and humans by physiologically based pharmacokinetic (PBPK) and quantum mechanics (QM) models with integrated multi-source data. LNPs with different ionizable lipids, particle sizes, and doses were modeled and compared by recognizing their critical parameters dominating PK. Some interesting results were found by the models. For example, the metabolism of ionizable lipids was first limited by the LNP disassembly rate instead of the hydrolyzation of ionizable lipids; the ability of RNA release from endosomes for three ionizable lipids was quantitively derived and can predict the probability of RNA release. Moreover, the biodegradability of three ionizable lipids was estimated by the QM method and the is generally consistent with the result of PBPK result. In summary, the transportation model of RNA LNP among various species for the first time was successfully constructed. Various in vitro and in vivo pieces of evidence were integrated through QM/PBPK multi-level modeling. The resulting new understandings are related to biodegradability, safety, and RNA release ability which are highly concerned issues of the formulation. This would benefit the design and research of RNA-LNP in the future.

Labetalol has an irreplaceable role in treating Hypertensive disorders of pregnancy (HDP), a common disease during pregnancy with a prevalence of 5.2–8.2

Objective This study aims to develop physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) predictive models for nifedipine in pregnant women, enhancing precision medicine and reducing adverse reactions for both mothers and infants. Methods A PBPK/PD model was constructed using PK-Sim, MoBi, and MATLAB software, integrating literature and pregnancy-specific physiological information. The process involved: (1) establishing and validating a PBPK model for serum clearance after intravenous administration in non-pregnant individuals, (2) establishing and validating a PBPK model for serum clearance after oral administration in non-pregnant individuals, (3) constructing and validating a PBPK model for enzyme clearance after oral administration in non-pregnant individuals, and (4) adjusting the PBPK model structure and enzyme parameters according to pregnant women and validating it in oral administration. (5) PK/PD model was explored through MATLAB, and the PBPK and PK/PD models were integrated to form the PBPK/PD model. Results The Nifedipine PBPK model's predictive accuracy was confirmed by non-pregnant and pregnant validation studies. The developed PBPK/PD model accurately predicted maximum antihypertensive effects for clinical doses of 5, 10, and 20 mg. The model suggested peak effect at 0.86 h post-administration, achieving blood pressure reductions of 5.4 mmHg, 14.3 mmHg, and 21.3 mmHg, respectively. This model provides guidance for tailored dosing in pregnancy-induced hypertension based on targeted blood pressure reduction. Conclusion Based on available literature data, the PBPK/PD model of Nifedipine in pregnancy demonstrated good predictive performance. It will help optimize individualized dosing of Nifedipine, improve treatment outcomes, and minimize the risk of adverse reactions in mothers and infants.

The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.

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.

Purpose Cyclodextrin (CD) is commonly used to enhance the solubility of oral drugs. However, with the increase of CD concentrations, the fraction of free drug molecules decreases, which may potentially impede drug absorption. This study aims to predict the optimal ratio between drug and CD to achieve the best absorption efficiency by computational simulation. Methods First, a physiologically based pharmacokinetic (PBPK) model was developed. This model can continuously adjust absorption according to free drug fraction and was validated against two model drugs, progesterone (PG) and andrographolide (AG). The further analysis involves 3-D surface graphs to investigate the relationship between free drug amount, theoretically absorbable concentration, and contents of drug and CD in the formulation. Results The PBPK model predicted the PK behavior of two drugs well. The concentration ratio of drug to CD, leading to maximal free drug amount and the best absorption efficiency, is nearly the same as the slope determined in the phase solubility test. The new modified PBPK model and 3-D surface graph can easily predict the absorption difference of formulations with various drug/CD ratios. Conclusion This PBPK model and 3-D surface graph can predict the absorption and determine the optimal concentration ratio of CD formulation, which could accelerate the R&D of CD formulation.

In recent decades pharmaceutics and drug delivery have become increasingly critical in the pharmaceutical industry due to longer time, higher cost, and less productivity of new molecular entities (NMEs). However, current formulation development still relies on traditional trial-and-error experiments, which are time-consuming, costly, and unpredictable. With the exponential growth of computing capability and algorithms, in recent ten years, a new discipline named "computational pharmaceutics" integrates with big data, artificial intelligence, and multi-scale modeling techniques into pharmaceutics, which offered great potential to shift the paradigm of drug delivery. Computational pharmaceutics can provide multi-scale lenses to pharmaceutical scientists, revealing physical, chemical, mathematical, and data-driven details ranging across pre-formulation studies, formulation screening, in vivo prediction in the human body, and precision medicine in the clinic. The present paper provides a comprehensive and detailed review in all areas of computational pharmaceutics and "Pharma 4.0", including artificial intelligence and machine learning algorithms, molecular modeling, mathematical modeling, process simulation, and physiologically based pharmacokinetic (PBPK) modeling. We not only summarized the theories and progress of these technologies but also discussed the regulatory requirements, current challenges, and future perspectives in the area, such as talent training and a culture change in the future pharmaceutical industry.

Drugs in solid dispersion (SD) take advantage of fast and extended dissolution, thus attains a higher bioavailability than the crystal form. However, current development of SD relies on a random large-scale formulation screening method with low efficiency. Current research aims to integrate various computational tools, including machine learning (ML), molecular dynamic (MD) simulation and physiologically based pharmacokinetic (PBPK) modeling, to accelerate the development of SD formulations. Firstly, based on a dataset consisting of 674 dissolution profiles of SD, the random forest algorithm was used to construct a classification model to distinguish two types of dissolution profiles: “spring-and-parachute” and “maintain supersaturation”, and a regression model to predict the time-dependent dissolution profiles. Both of the two prediction models showed good prediction performance. Moreover, feature importance was performed to help understand the key information that contributes to the model. After that, the vemurafenib (VEM) SD formulation in previous report was used as an example to validate the models. MD simulation was used to investigate the dissolution behavior of two SD formulations with two polymers (HPMCAS and Eudragit) at the molecular level. The results showed that the HPMCAS-based formulation resulted in faster dissolution than the Eudragit formulation, which agreed with the reported experimental results. Finally, a PBPK model was constructed to accurately predict the human pharmacokinetic profile of the VEM-HPMCAS SD formulation. In conclusion, combined computational tools have been developed to in silico predict formulation composition, in vitro release and in vivo absorption behavior of SD formulations. The integrated computational methodology will significantly facilitate pharmaceutical formulation development than the traditional trial-and-error approach in the laboratory.

Current formulation development strongly relies on trial-and-error experiments in the laboratory by pharmaceutical scientists, which is time-consuming, high cost and waste materials. This research aims to integrate various computational tools, including machine learning, molecular dynamic simulation and physiologically based absorption modeling (PBAM), to enhance andrographolide (AG) /cyclodextrins (CDs) formulation design. The lightGBM prediction model we built before was utilized to predict AG/CDs inclusion's binding free energy. AG/γ-CD inclusion complexes showed the strongest binding affinity, which was experimentally validated by the phase solubility study. The molecular dynamic simulation was used to investigate the inclusion mechanism between AG and γ-CD, which was experimentally characterized by DSC, FTIR and NMR techniques. PBAM was applied to simulate the in vivo behavior of the formulations, which were validated by cell and animal experiments. Cell experiments revealed that the presence of D-α-Tocopherol polyethylene glycol succinate (TPGS) significantly increased the intracellular uptake of AG in MDCK-MDR1 cells and the absorptive transport of AG in MDCK-MDR1 monolayers. The relative bioavailability of the AG-CD-TPGS ternary system in rats was increased to 2.6-fold and 1.59-fold compared with crude AG and commercial dropping pills, respectively. In conclusion, this is the first time to integrate various computational tools to develop a new AG-CD-TPGS ternary formulation with significant improvement of aqueous solubility, dissolution rate and bioavailability. The integrated computational tool is a novel and robust methodology to facilitate pharmaceutical formulation design.

Lipid nanoparticle (LNP) is commonly used to deliver mRNA vaccines. Currently, LNP optimization primarily relies on screening ionizable lipids by traditional experiments which consumes intensive cost and time. Current study attempts to apply computational methods to accelerate the LNP development for mRNA vaccines. Firstly, 325 data samples of mRNA vaccine LNP formulations with IgG titer were collected. The machine learning algorithm, lightGBM, was used to build a prediction model with good performance (R 2 > 0.87). More importantly, the critical substructures of ionizable lipids in LNPs were identified by the algorithm, which well agreed with published results. The animal experimental results showed that LNP using DLin-MC3-DMA (MC3) as ionizable lipid with an N/P ratio at 6:1 induced higher efficiency in mice than LNP with SM-102, which was consistent with the model prediction. Molecular dynamic modeling further investigated the molecular mechanism of LNPs used in the experiment. The result showed that the lipid molecules aggregated to form LNPs, and mRNA molecules twined around the LNPs. In summary, the machine learning predictive model for LNP-based mRNA vaccines was first developed, validated by experiments, and further integrated with molecular modeling. The prediction model can be used for virtual screening of LNP formulations in the future.