Design of improved permeation enhancers for transdermal drug delivery

Drug Permeability: From the Blood-Brain Barrier to the Peripheral Nerve Barriers

Drug delivery into the peripheral nerves and nerve roots has important implications for effective local anesthesia and treatment of peripheral neuropathies and chronic neuropathic pain. Similar to drugs that need to cross the blood-brain barrier (BBB) and blood-spinal cord barrier (BSCB) to gain access to the central nervous system (CNS), drugs must cross the peripheral nerve barriers (PNB), formed by the perineurium and blood-nerve barrier (BNB) to modulate peripheral axons. Despite significant progress made to develop effective strategies to enhance BBB permeability in therapeutic drug design, efforts to enhance drug permeability and retention in peripheral nerves and nerve roots are relatively understudied. Guided by knowledge describing structural, molecular and functional similarities between restrictive neural barriers in the CNS and peripheral nervous system (PNS), we hypothesize that certain CNS drug delivery strategies are adaptable for peripheral nerve drug delivery. In this review, we describe the molecular, structural and functional similarities and differences between the BBB and PNB, summarize and compare existing CNS and peripheral nerve drug delivery strategies, and discuss the potential application of selected CNS delivery strategies to improve efficacious drug entry for peripheral nerve disorders.

Prioritization based on risk assessment to study the bioconcentration and biotransformation of pharmaceuticals in glass eels (Anguilla anguilla) from the Adour estuary (Basque Country, France)

The presence of contaminants of emerging concern in the aquatic environment directly impacts water-living organisms and can alter their living functions. These compounds are often metabolized and excreted, but they can also be accumulated and spread through the food chain. The metabolized contaminants can also lead to the formation of new compounds with unknown toxicity and bioaccumulation potential. In this work, we have studied the occurrence, bioconcentration, and biotransformation of CECs in glass eels (Anguilla anguilla) using UHPLC-HRMS. To select the target CECs, we first carried out an environmental risk assessment of the WWTP effluent that releases directly into the Adour estuary (Bayonne, Basque Country, France). The risk quotients of every detected contaminant were calculated and three ecotoxicologically relevant contaminants were chosen to perform the exposure experiment: propranolol, diazepam, and irbesartan. An experiment of 14 days consisting of 7 days of exposure and 7 days of depuration was carried out to measure the bioconcentration of the chosen compounds. The quantitative results of the concentrations in glass eel showed that diazepam and irbesartan reached BCF ≈10 on day 7, but both compounds were eliminated after 7 days of depuration. On the other hand, propranolol's concentration remains constant all along with the experiment, and its presence can be detected even in the non-exposed control group, which might suggest environmental contamination. Two additional suspect screening strategies were used to identify metabolization products of the target compounds and other xenobiotics already present in wild glass eels. Only one metabolite was identified, nordiazepam, a well-known diazepam metabolite, probably due to the low metabolic rate of glass eels at this stage. The xenobiotic screening confirmed the presence of more xenobiotics in wild glass eels, prominent among them, the pharmaceuticals exemestane, primidone, iloprost, and norethandrolone.

Potential of Raman spectroscopy in facilitating pharmaceutical formulations development - An AI perspective

Drug development is time-consuming and inherently possesses a high failure rate. Pharmaceutical formulation development is the bridge that links a new chemical entity (NCE) to pre-clinical and clinical trials, and has a high impact on the efficacy and safety of the final drug product. Further, the time required for this process is escalating as formulation techniques are becoming more complicated due to the rising demands for drug products with better efficacy and patient compliance, as well as the inherent difficulties of addressing the unfavorable properties of NCEs such as low water solubility. The advent of artificial intelligence (AI) provides possibilities to accelerate the drug development process. In this review, we first examine applications of AI methods in different types of pharmaceutical formulations and formulation techniques. Moreover, as availability of data is the engine for the advancement of AI, we then suggest a potential way (i.e. applying Raman spectroscopy) for faster high-quality data gathering from formulations. Raman techniques have the capability of analyzing the composition and distribution of components and the physicochemical properties thereof within formulations, which are prominent factors governing drug dissolution profiles and subsequently bioavailability. Thus, useful information can be obtained bridging formulation development to the final product quality.

The significance of artificial intelligence in drug delivery system design

Over the last decade, increasing interest has been attracted towards the application of artificial intelligence (AI) technology for analyzing and interpreting the biological or genetic information, accelerated drug discovery, and identification of the selective small-molecule modulators or rare molecules and prediction of their behavior. Application of the automated workflows and databases for rapid analysis of the huge amounts of data and artificial neural networks (ANNs) for development of the novel hypotheses and treatment strategies, prediction of disease progression, and evaluation of the pharmacological profiles of drug candidates may significantly improve treatment outcomes. Target fishing (TF) by rapid prediction or identification of the biological targets might be of great help for linking targets to the novel compounds. AI and TF methods in association with human expertise may indeed revolutionize the current theranostic strategies, meanwhile, validation approaches are necessary to overcome the potential challenges and ensure higher accuracy. In this review, the significance of AI and TF in the development of drugs and delivery systems and the potential challenging issues have been highlighted.

Metabolism of N-Methyl-2-Pyrrolidone in Honey Bee Adults and Larvae: Exploring Age Related Differences in Toxic Effects

In chronic feeding assays, the common agrochemical inert formulant N-methyl-2-pyrrolidone (NMP) is at least 20 times more toxic to honey bee larvae than to adults, but the underlying cause of this difference is unknown. In other taxa, NMP is primarily detoxified via a cytochrome P450 mediated pathway. Using a LC-MS method, putative cytochrome P450 metabolites of NMP were identified and quantified in adults and larvae following chronic exposure to NMP. Major differences in the identities and quantities of the generated metabolites were observed between adults and larvae. One major difference was the higher percentage of the administered NMP recovered as the parent compound in larvae compared to adults. To further explore the apparent difference in metabolic capacity, a spectrofluorometric method was used to compare general cytochrome P450 enzyme activity by monitoring the transformation of a 7-ethoxycoumarin substrate. Higher microsomal levels of 7-ethoxycoumarin-O-deethylase activity in adult fat bodies suggests that the higher percentage of unmetabolized NMP in larvae relative to adults may be due to lower cytochrome P450 enzyme activity in fat bodies. Taken together, these results suggest that larvae may be less able to detoxify xenobiotics encountered in diet than adults, and these findings will help inform future risk assessment.

Chemical penetration enhancers

To achieve an efficient skin penetration of most compounds it is necessary to overcome the barrier function of the skin, provided mainly (but not only) by the stratum corneum. Among various strategies used or studied to date, chemical penetration enhancers are the most frequently employed with one of the longest histories of use. There is a multitude of agents described as penetration enhancers, and they present varying properties and structures. In this manuscript, we aim to provide a brief overview of traditional enhancers and some of their properties, focusing on the benefits of combination of chemical enhancers and on selected novel compounds that have shown promise to increase drug delivery into/across the skin.

In Silico Prediction of Mechanism of Action for Cancer Therapeutics

Cancer is currently the second leading cause of death in the U.S. and is projected to become the principal cause in the near future. While radiation and surgery are common cancer treatment methods, chemotherapy remains a key treatment option, offering distinct advantages over other therapy options, especially in the management of metastasized tumors. Understanding the mechanism of action (MoA) of current and newly developed drugs is crucial to ongoing drug development research. Foreknowledge of how a candidate drug works can yield a wealth of information, including which cancers a drug may treat more effectively based on the susceptibility of the cancer to drugs with the same MoA. Previous studies concerning prediction of MoA have relied on costly experimental measurements as input for their predictions. We have developed an a priori quantitative structure-activity relationship (QSAR) for the in silico prediction of MoA without the need for experimental measurements. This model enables us to relate structural features of a chemical to its efficacy with a predictive accuracy of over 80 %, thus identifying the MoA of a candidate drug without costly, time-consuming experimental tests.

Virtual design of chemical penetration enhancers for transdermal drug delivery

Traditional drug design is a laborious and expensive process that often challenges the pharmaceutical industries. As a result, researchers have turned to computational methods for computer-assisted molecular design. Recently, genetic and evolutionary algorithms have emerged as efficient methods in solving combinatorial problems associated with computer-aided molecular design. Further, combining genetic algorithms with quantitative structure-property relationship analyses has proved effective in drug design. In this work, we have integrated a new genetic algorithm and nonlinear quantitative structure-property relationship models to develop a reliable virtual screening algorithm for the generation of potential chemical penetration enhancers. The genetic algorithms-quantitative structure-property relationship algorithm has been implemented successfully to identify potential chemical penetration enhancers for transdermal drug delivery of insulin. Validation of the newly identified chemical penetration enhancer molecular structures was conducted through carefully designed experiments, which elucidated the cytotoxicity and permeability of the chemical penetration enhancers.

Rank-based ant system method for non-linear QSPR analysis: QSPR studies of the solubility parameter

The solubility parameter (δ) plays a unique role in the development of stable pharmaceutical formulations for assessing phase segregation during product synthesis. Understanding this parameter helps to determine how a drug substance will behave when processed or when dosed in vivo. The aim of this work was to develop a novel comprehensive yet rapid and accurate Quantitative Structure-Property Relationship (QSPR) method based on the rank-based ant system feature selection. The method was coupled with the multiple linear regression and support vector regression and applied to the assessment of solubility parameters for a diverse dataset of 1804 chemical compounds. The models were validated by solubility prediction of 360 test set compounds which were not used in building models. The developed models have high prediction power characterized by r (2) values 0.75 and 0.82, and RMSE values 1.96 and 1.65 (J/(cm(3)))(0.5) for the external test set. Various validation techniques and comparison results with the novel optimized support vector regression indicate that the developed models can be used to determine the solubility parameters for a diverse set of chemicals with an acceptable accuracy. The developed models can be beneficial for designing new chemical materials with desired solubility parameter values.

Effect of different enhancers on the transdermal permeation of insulin analog

Using chemical penetration enhancers (CPEs), transdermal drug delivery (TDD) offers an alternative route for insulin administration, wherein the CPEs reversibly reduce the barrier resistance of the skin. However, there is a lack of sufficient information concerning the effect of CPE chemical structure on insulin permeation. To address this limitation, we examined the effect of CPE functional groups on the permeation of insulin. A virtual design algorithm that incorporates quantitative structure-property relationship (QSPR) models for predicting the CPE properties was used to identify 43 potential CPEs. This set of CPEs was pre-screened using a resistance technique, and the 22 best CPEs were selected. Next, standard permeation experiments in Franz cells were performed to quantify insulin permeation. Our results indicate that specific functional groups are not directly responsible for enhanced insulin permeation. Rather, permeation enhancement is produced by molecules that exhibit positive logK(ow) values and possess at least one hydrogen donor or acceptor. Toluene was the only exception among the 22 potential CPEs considered. In addition, toxicity analyses of the 22 CPEs were performed. A total of eight CPEs were both highly enhancing (permeability coefficient at least four times the control value) and non-toxic, five of which are new discoveries.

A skin permeability model of insulin in the presence of chemical penetration enhancer

Enhancing transdermal delivery of insulin using chemical penetration enhancers (CPEs) has several advantages over other non-traditional methods; however, lack of suitable predictive models, make experimentation the only alternative for discovering new CPEs. To address this limitation, a quantitative structure-property relationship (QSPR) model was developed, for predicting insulin permeation in the presence of CPEs. A virtual design algorithm that incorporates QSPR models for predicting CPE properties was used to identify 48 potential CPEs. Permeation experiments using Franz diffusion cells and resistance experiments were performed to quantify the effect of CPEs on insulin permeability and skin structure, respectively. Of the 48 CPEs, 35 were used for training and 13 were used for validation. In addition, 12 CPEs reported in literature were also included in the validation set. Differential evolution (DE) was coupled with artificial neural networks (ANNs) to develop the non-linear QSPR models. The six-descriptor model had a 16% absolute average deviation (%AAD) in the training set and 4 misclassifications in the validation set. Five of the six descriptors were found to be statistically significant after sensitivity analyses. The results suggest, molecules with low dipoles that are capable of forming intermolecular bonds with skin lipid bi-layers show promise as effective insulin-specific CPEs.