Turning down the heat: The case for mild hyperthermia and thermosensitive liposomes.

"A single disappointing study does not mean an end to the future of ThermoDox®", writes Michael Tardugno (CEO of Celsion Corporation), after announcing the termination of Celsion's second Phase III clinical trial. The OPTIMA trial, as it was known, evaluated their thermosensitive liposome (TSL) formulation of doxorubicin (ThermoDox®) in combination with radiofrequency ablation for the treatment of hepatocellular carcinoma (HCC). The purpose of this perspective is to review the case of ThermoDox and to address questions related to its clinical translation. Specifically, what has prevented the clinical translation of this once highly regarded breakthrough technology? Is this the end of TSLs? What can we learn from the challenges faced in the clinical development of this multi-modal therapy? As formulation scientists working in the field, we continue to believe that heat-triggered drug delivery platforms have tremendous potential as chemotherapy. Herein, we highlight potential limitations in the design of many of the Thermodox clinical trials, and we propose that despite these setbacks, TSLs have the potential to become an effective component of cancer therapy.

The Dawn of a New Pharmaceutical Epoch: Can AI and Robotics Reshape Drug Formulation?

Over the last four decades, pharmaceutical companies' expenditures on research and development have increased 51-fold. During this same time, clinical success rates for new drugs have remained unchanged at about 10 percent, predominantly due to lack of efficacy and/or safety concerns. This persistent problem underscores the need to innovate across the entire drug development process, particularly in drug formulation, which is often deprioritized and under-resourced.

Data-driven development of an oral lipid-based nanoparticle formulation of a hydrophobic drug.

Due to its cost-effectiveness, convenience, and high patient adherence, oral drug administration normally remains the preferred approach. Yet, the effective delivery of hydrophobic drugs via the oral route is often hindered by their limited water solubility and first-pass metabolism. To mitigate these challenges, advanced delivery systems such as solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been developed to encapsulate hydrophobic drugs and enhance their bioavailability. However, traditional design methodologies for these complex formulations often present intricate challenges because they are restricted to a relatively narrow design space. Here, we present a data-driven approach for the accelerated design of SLNs/NLCs encapsulating a model hydrophobic drug, cannabidiol, that combines experimental automation and machine learning. A small subset of formulations, comprising 10% of all formulations in the design space, was prepared in-house, leveraging miniaturized experimental automation to improve throughput and decrease the quantity of drug and materials required. Machine learning models were then trained on the data generated from these formulations and used to predict properties of all SLNs/NLCs within this design space (i.e., 1215 formulations). Notably, formulations predicted to be high-performers via this approach were confirmed to significantly enhance the solubility of the drug by up to 3000-fold and prevented degradation of drug. Moreover, the high-performance formulations significantly enhanced the oral bioavailability of the drug compared to both its free form and an over-the-counter version. Furthermore, this bioavailability matched that of a formulation equivalent in composition to the FDA-approved product, Epidiolex®.

Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence.

INTRODUCTION: Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such formulations is non-trivial and largely reliant on trial-and-error. Beyond the costly demands on time and resources, this traditional approach may stunt innovation. The emergence of automation, artificial intelligence (AI) and machine learning (ML) tools, which are currently underutilized in pharmaceutical formulation development, offers a promising direction for an improved path in the design of nanomedicines. AREAS COVERED: the potential of harnessing experimental automation and AI/ML to drive innovation in nanomedicine development. The discussion centers on the current challenges in drug formulation research and development, and the major advantages afforded through the application of data-driven methods. EXPERT OPINION: The development of integrated workflows based on automated experimentation and AI/ML may accelerate nanomedicine development. A crucial step in achieving this is the generation of high-quality, accessible datasets. Future efforts to make full use of these tools can ultimately contribute to the development of more innovative nanomedicines and improved clinical translation of formulations that rely on advanced drug delivery systems.

Harnessing immunotherapy to enhance the systemic anti-tumor effects of thermosensitive liposomes.

Chemotherapy plays an important role in debulking tumors in advance of surgery and/or radiotherapy, tackling residual disease, and treating metastatic disease. In recent years many promising advanced drug delivery strategies have emerged that offer more targeted delivery approaches to chemotherapy treatment. For example, thermosensitive liposome-mediated drug delivery in combination with localized mild hyperthermia can increase local drug concentrations resulting in a reduction in systemic toxicity and an improvement in local disease control. However, the majority of solid tumor-associated deaths are due to metastatic spread. A therapeutic approach focused on a localized target area harbors the risk of overlooking and undertreating potential metastatic spread. Previous studies reported systemic, albeit limited, anti-tumor effects following treatment with thermosensitive liposomal chemotherapy and localized mild hyperthermia. This work explores the systemic treatment capabilities of a thermosensitive liposome formulation of the vinca alkaloid vinorelbine in combination with mild hyperthermia in an immunocompetent murine model of rhabdomyosarcoma. This treatment approach was found to be highly effective at heated, primary tumor sites. However, it demonstrated limited anti-tumor effects in secondary, distant tumors. As a result, the addition of immune checkpoint inhibition therapy was pursued to further enhance the systemic anti-tumor effect of this treatment approach. Once combined with immune checkpoint inhibition therapy, a significant improvement in systemic treatment capability was achieved. We believe this is one of the first studies to demonstrate that a triple combination of thermosensitive liposomes, localized mild hyperthermia, and immune checkpoint inhibition therapy can enhance the systemic treatment capabilities of thermosensitive liposomes.

Self-driving laboratories: A paradigm shift in nanomedicine development.

Nanomedicines have transformed promising therapeutic agents into clinically approved medicines with optimal safety and efficacy profiles. This is exemplified by the mRNA vaccines against COVID-19, which were made possible by lipid nanoparticle technology. Despite the success of nanomedicines to date, their design remains far from trivial, in part due to the complexity associated with their preclinical development. Herein, we propose a nanomedicine materials acceleration platform (NanoMAP) to streamline the preclinical development of these formulations. NanoMAP combines high-throughput experimentation with state-of-the-art advances in artificial intelligence (including active learning and few-shot learning) as well as a web-based application for data sharing. The deployment of NanoMAP requires interdisciplinary collaboration between leading figures in drug delivery and artificial intelligence to enable this data-driven design approach. The proposed approach will not only expedite the development of next-generation nanomedicines but also encourage participation of the pharmaceutical science community in a large data curation initiative.

Data demonstrating the in vivo anti-tumor efficacy of thermosensitive liposome formulations of a drug combination in pre-clinical models of breast cancer.

Thermosensitive liposomes in combination with localized mild hyperthermia can improve the delivery of drug to solid tumor sites. For this reason, thermosensitive liposome formulations of a range of chemotherapy drugs have been designed. Our group previously developed and characterized a thermosensitive liposome formulation of the heat shock protein 90 inhibitor alvespimycin as a companion therapeutic to a thermosensitive liposome formulation equivalent in composition to ThermoDox (i.e., ThermoDXR), with the goal of increasing the therapeutic index of doxorubicin as the combination was revealed to be highly synergistic in a panel of human breast cancer cell lines including MDA-MB-231 (Dunne et al., 2019). The data presented here further describes the effect of the doxorubicin (DXR) and alvespimycin (ALV) combination in vitro and in vivo. Specifically, the combination effect in mouse breast cancer 4T1 cells and the in vivo efficacy of this heat-activated chemotherapy combination in both immunocompromised (MDA-MB-231 tumor bearing female SCID mice) and immunocompetent (4T1 tumor bearing female BALB/c mice) models of breast cancer.

Machine learning models to accelerate the design of polymeric long-acting injectables.

Long-acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it very difficult to intuitively predict the performance of these systems. This necessitates the development and characterization of a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. The current study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables. Here we show that machine learning algorithms can be used to predict experimental drug release from these advanced drug delivery systems. We also demonstrate that these trained models can be used to guide the design of new long acting injectables. The implementation of the described data-driven approach has the potential to reduce the time and cost associated with drug formulation development.

Triggered release from thermosensitive liposomes improves tumor targeting of vinorelbine.

Triggered drug delivery strategies have been shown to enhance drug accumulation at target diseased sites in comparison to administration of free drug. In particular, many studies have demonstrated improved targetability of chemotherapeutics when delivered via thermosensitive liposomes. However, most studies continue to focus on encapsulating doxorubicin while many other drugs would benefit from this targeted and localized delivery approach. The proposed study explores the therapeutic potential of a thermosensitive liposome formulation of the commonly used chemotherapy drug vinorelbine in combination with mild hyperthermia (39-43 °C) in a murine model of rhabdomyosarcoma. Rhabdomyosarcoma, the most common soft tissue sarcoma in children, is largely treated using conventional chemotherapy which is associated with significant adverse long-term sequelae. In this study, mild hyperthermia was pursued as a non-invasive, non-toxic means to improve the efficacy and safety profiles of vinorelbine. Thorough assessment of the pharmacokinetics, biodistribution, efficacy and toxicity of vinorelbine administered in the thermosensitive liposome formulation was compared to administration in a traditional, non-thermosensitive liposome formulation. This study shows the potential of an advanced formulation technology in combination with mild hyperthermia as a means to target an untargeted therapeutic agent and result in a significant improvement in its therapeutic index.

Data evaluating triamcinolone acetonide and triamcinolone hexacetonide loaded poly(δ-valerolactone-co-allyl-δ-valerolactone) microparticles.

Advanced drug delivery strategies can be used to enhance the therapeutic effectiveness of locally delivered corticosteroids. Poly(δ-valerolactone-co-allyl-δ-valerolactone) microparticles (PVL-co-PAVL MPs) were evaluated for delivery of two corticosteroids, triamcinolone acetonide and triamcinolone hexacetonide. PVL-co-PAVL MPs were prepared using a modified oil-in-water emulsification method, followed by a UV-initiated cross-linking process. The resulting PVL-co-PAVL MPs were purified with an excess amount of water and then acetone to remove residual surfactant, cross-linker, and catalyst before lyophilization. Triamcinolone acetonide and triamcinolone hexacetonide were independently loaded into the resulting PVL-co-PAVL MPs via a post-loading swelling-equilibrium method. The drug-loaded MPs were characterized in terms of drug loading (determined by high-performance liquid chromatography, HPLC), thermal properties (determined by differential scanning calorimetry, DSC), and in vitro drug release kinetics (with quantification of drug using HPLC) to better understand the suitability of PVL-co-PAVL MPs for delivery of corticosteroids. These data demonstrate the potential of PVL-co-PAVL MPs as a promising drug delivery platform for the sustained release of corticosteroids. Raw data have been made available on Mendeley Data. Additional details on PVL-co-PAVL MPs were previously reported [1].

Revolutionizing drug formulation development: The increasing impact of machine learning.

Over the past few years, the adoption of machine learning (ML) techniques has rapidly expanded across many fields of research including formulation science. At the same time, the use of lipid nanoparticles to enable the successful delivery of mRNA vaccines in the recent COVID-19 pandemic demonstrated the impact of formulation science. Yet, the design of advanced pharmaceutical formulations is non-trivial and primarily relies on costly and time-consuming wet-lab experimentation. In 2021, our group published a review article focused on the use of ML as a means to accelerate drug formulation development. Since then, the field has witnessed significant growth and progress, reflected by an increasing number of studies published in this area. This updated review summarizes the current state of ML directed drug formulation development, introduces advanced ML techniques that have been implemented in formulation design and shares the progress on making self-driving laboratories a reality. Furthermore, this review highlights several future applications of ML yet to be fully exploited to advance drug formulation research and development.

Shifting the Paradigm on Cannabis Safety.

The global movement toward legalization of cannabis is resulting in an ever-increasing public perception that cannabis is safe. Cannabis is not the first drug to be available for nonmedical use, nor is it the first to have such an unfounded safety profile. The safety of long-term exposure to phytocannabinoids is misunderstood by, and under reported to, the general public. There is evidence to suggest that long-term use of recreational cannabis may be associated with an increased risk of undesirable side effects. This evidence warrants both appropriate caution from the general public and investment in further research by government and industry sectors that are profiting from the sale of these potent psychoactive agents. There is no doubt that these compounds have medical potential. However, in addition to the medical potential, we must also remain aware of the adverse health effects that are becoming synonymous with recreational cannabis use. This perspective highlights the privileged role that cannabis has as a perceived "safe drug" in society and summarizes some concerning side effects that are becoming associated with regular nonprescribed cannabis use.

Development and pharmacokinetic evaluation of a self-nanoemulsifying drug delivery system for the oral delivery of cannabidiol.

The number of lipophilic drug candidates in pharmaceutical discovery pipelines has increased in recent years. These drugs often possess physicochemical properties that result in poor oral bioavailability, and their clinical potential may be limited without adequate formulation strategies. Cannabidiol (CBD) is an excellent example of a highly lipophilic compound with poor oral bioavailability, due to low water solubility and extensive first-pass metabolism. An approach that may overcome these limitations is formulation of the drug in self-nanoemulsifying drug delivery systems (SNEDDS). Herein, CBD-SNEDDS formulations were prepared and evaluated in vitro. Promising formulations (F2, F4) were administered to healthy female Sprague-Dawley rats via oral gavage (20 mg/kg CBD). Resulting pharmacokinetic parameters of CBD were compared to those obtained following administration of CBD in two oil-based formulations: a medium-chain triglyceride oil vehicle (MCT-CBD), and a sesame oil-based formulation similar in composition to an FDA-approved formulation of CBD, Epidiolex® (SO-CBD). Compared to MCT-CBD, administration of the SNEDDS formulations led to more rapid absorption of CBD (median Tmax values: 0.5 h (F2), 1 h (F4), 6 h (MCT-CBD)). Administration of F2 and F4 formulations also improved the systemic exposure to CBD by 2.2 and 2.8-fold compared to MCT-CBD; however, no improvement was found compared to SO-CBD.

Machine learning directed drug formulation development.

Machine learning (ML) has enabled ground-breaking advances in the healthcare and pharmaceutical sectors, from improvements in cancer diagnosis, to the identification of novel drugs and drug targets as well as protein structure prediction. Drug formulation is an essential stage in the discovery and development of new medicines. Through the design of drug formulations, pharmaceutical scientists can engineer important properties of new medicines, such as improved bioavailability and targeted delivery. The traditional approach to drug formulation development relies on iterative trial-and-error, requiring a large number of resource-intensive and time-consuming in vitro and in vivo experiments. This review introduces the basic concepts of ML-directed workflows and discusses how these tools can be used to aid in the development of various types of drug formulations. ML-directed drug formulation development offers unparalleled opportunities to fast-track development efforts, uncover new materials, innovative formulations, and generate new knowledge in drug formulation science. The review also highlights the latest artificial intelligence (AI) technologies, such as generative models, Bayesian deep learning, reinforcement learning, and self-driving laboratories, which have been gaining momentum in drug discovery and chemistry and have potential in drug formulation development.

Heat-activated nanomedicine formulation improves the anticancer potential of the HSP90 inhibitor luminespib in vitro.

The heat shock protein 90 inhibitor, luminespib, has demonstrated potent preclinical activity against numerous cancers. However, clinical translation has been impeded by dose-limiting toxicities that have necessitated dosing schedules which have reduced therapeutic efficacy. As such, luminespib is a prime candidate for reformulation using advanced drug delivery strategies that improve tumor delivery efficiency and limit off-target side effects. Specifically, thermosensitive liposomes are proposed as a drug delivery strategy capable of delivering high concentrations of drug to the tumor in combination with other chemotherapeutic molecules. Indeed, this work establishes that luminespib exhibits synergistic activity in lung cancer in combination with standard of care drugs such as cisplatin and vinorelbine. While our research team has previously developed thermosensitive liposomes containing cisplatin or vinorelbine, this work presents the first liposomal formulation of luminespib. The physico-chemical properties and heat-triggered release of the formulation were characterized. Cytotoxicity assays were used to determine the optimal drug ratios for treatment of luminespib in combination with cisplatin or vinorelbine in non-small cell lung cancer cells. The formulation and drug combination work presented in this paper offer the potential for resuscitation of the clinical prospects of a promising anticancer agent.

The impact of endogenous gastrointestinal molecules on the dissolution and precipitation of orally delivered hydrophobic APIs.

Introduction: Given that hydrophobic active pharmaceutical ingredients (APIs) intended for oral delivery comprised about 68% of US FDA approvals in 2019 alone, the impact of endogenous gastrointestinal (GI) molecules on their inherently unstable solution behavior needs to be elucidated.Areas covered: The interactions between hydrophobic API's and GI phospholipids, bile acids/salts and digestive proteins are explored. The impact of the complex relationship between the GI molecules and hydrophobic APIs on solubilization by micelle formation, complexation or by inhibiting the nucleation of high energy forms of hydrophobic APIs, so called supersaturating drug delivery systems is complex. The ability of these endogenous GI molecules to manipulate the solution behavior of hydrophobic APIs has been demonstrated both at their native concentrations and when included as exogenous formulation additives. Specific studies of the impact of proteins and mixed micelles on solubilization and crystallization are reported.Expert opinion: Elucidation of the complex molecular interactions between orally administered hydrophobic APIs and endogenous GI molecules will enable better in vivo/in vitro correlation and potentially lead to formulation strategies that avoid the stochastic nature of hydrophobic API precipitation in the GI tract.

Investigating the effects of amphipathic gastrointestinal compounds on the solution behaviour of salt and free base forms of clofazimine: An in vitro evaluation.

Interactions between hydrophobic drugs and endogenous gastrointestinal substances have the potential to manipulate drug concentration in the human gastrointestinal system, and thus likely play an important role in determining the rate of absorption for hydrophobic drugs. The effects of phospholipids, bile salts and digestive proteins on the solution behaviour of clofazimine in biorelevant media was demonstrated here using dissolution experiments and solid state analytical techniques. Clofazimine is a hydrophobic, anti-mycobacterial agent with virtually no detectable water solubility in its free base form. Salt forms of the drug offer improved aqueous solubility but are unstable in solutions at low pH (pH 1.6) or high pH (pH 6.5). At low pH and high chloride ion concentrations, CFZ in solution experiences a high driving force to crystallize from solution as a hydrochloride salt, which is insoluble, while at high pH CFZ does not dissolve to any extent. In this study, it is demonstrated that amphipathic compounds present in the gastric and intestinal systems can overcome the instability experienced by CFZ at these pH values. This is done by encapsulation of the hydrophobic drug in mixed bile salt phospholipid micelles in both the gastric and intestinal fluid, and by the drug actively binding with the digestive enzyme pepsin in the gastric system. Pepsin binds and solubilises the drug at even relatively low concentration (0.1 mg/mL). When pepsin concentration is increased in the gastric media, a corresponding increase in the solution stability of CFZ is observed.

Delivery of a hydrophobic drug into the lower gastrointestinal system via an endogenous enzyme-mediated carrier mechanism: An in vitro study.

Clofazimine (CFZ) is a hydrophobic antibiotic agent which exhibits poor solubility. This poor solubility was overcome herein by the formulation of CFZ with the digestive enzyme pepsin. It is shown that pepsin can actively bind 11 CFZ molecules in the protein's native gastric environment, forming a CFZ-pepsin complex. A dynamic dissolution system, representing both the gastric and intestinal system, was used to analyze this CFZ-pepsin complex, revealing that only CFZ which binds to pepsin in the gastric environment remains in solution in the intestinal environment. The CFZ-pepsin complex displays adequate solution stability for the delivery of CFZ into the lower intestinal system. In vitro bioactivity assays against Clostridium difficile demonstrated the effectiveness of this CFZ-pepsin complex for the treatment of infectious diseases in the lower intestinal system.

Role of Biorelevant Dissolution Media in the Selection of Optimal Salt Forms of Oral Drugs: Maximizing the Gastrointestinal Solubility and in Vitro Activity of the Antimicrobial Molecule, Clofazimine.

Clofazimine is an antimycobacterial agent that is routinely used for the treatment of leprosy. Clofazimine has also been shown to have high clinical potential for the treatment of many Gram-positive pathogens, including those that exhibit high levels of antibiotic resistance in the medical community. The use of clofazimine against these pathogens has largely been limited by the inherently poor water solubility of the drug substance. In this work, the possibility of repurposing and reformulating clofazimine to maximize its clinical potential is investigated. To achieve this, the potential of novel salt forms of clofazimine as supersaturating drug-delivery vehicles to enhance the aqueous solubility and gastrointestinal solubility of the drug substance was explored. The solution properties of seven novel salt forms, identified during an initial screening process, were examined in water and in a gastrointestinal-like media and were compared and contrasted with those of the free base, clofazimine, and the commercial formulation of the drug, Lamprene. The stability of the most promising solid forms was tested, and their bioactivity against Staphylococcus aureus was also compared with that of the clofazimine free base and Lamprene. Salts forms which showed superior stability as well as solubility and activity to the commercial drug formulation were fully characterized using a combination of spectroscopic techniques, including X-ray diffraction, solid-state NMR, and Fourier transform infrared spectroscopy.