Effect of Drug Loading Method on Drug Content and Drug Release from Calcium Pectinate Gel Beads

Drug-loaded calcium pectinate gel (CaPG) beads were prepared by either mixing, absorption, or swelling method. The effects of drug loading method as well as the drug loading factors (i.e., drug concentration, soaking time in drug solution, type of solvent) on drug content and drug release were investigated. The amount of drug uptake (i.e., drug content) into CaPG beads increased as the initial drug concentration increased and varied depending on the loading method. The in vitro release studies in 0.1 N hydrochloric acid (HCl) and pH 6.8 buffer indicated that the drug loading method affected drug release and release parameter, time for 50% of drug release (T ₅₀). The mixing method provided a faster drug release and lower T ₅₀ than the absorption method and swelling method, respectively. This is probably due to higher drug content in CaPG beads. The increased concentration of drug in soaking solution and soaking time resulted in higher drug content and thus faster drug release (lower in T ₅₀ values). When using 0.1 N HCl as solvent for soaking instead of water, the drug release was slower owing to the increase in molecular tortuosity of CaPG beads. The drug release was also affected by pH of the release medium in which drug release in 0.1 N HCl was faster than in pH 6.8 buffer.     

Prediction of the biological effect of magnetically regulated drugs

Kinetic aspects of magnetically targeted drug transport are discussed. The influence of the magnetic drug carrier properties, the transported drug and the target organ parameters on the biological effect of the drug was investigated. A mathematical model reflecting mass transfer processes in the circulatory system was used for estimating the influence of these factors on the therapeutic effect of the drug delivered to the target. The nature of the effect of the drug release parameters, inactivation rate of the released drug in the blood and intensity of the carrier capture out of the target organ on the drug therapeutic action was revealed. Mathematical modeling of magnetically targeted drug kinetics was shown to provide prognostication of the drug effect and increasing of the experiment informative capacity.     

Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy

Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients’ (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future.     

PREDICT: a method for inferring novel drug indications with application to personalized medicine

Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large‐scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug–drug and disease–disease similarity measures for the prediction task. On cross‐validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug indications that are currently under clinical trials, and by their agreement with tissue‐specific expression information on the drug targets. We further show that disease‐specific genetic signatures can be used to accurately predict drug indications for new diseases (AUC=0.92). This lays the computational foundation for future personalized drug treatments, where gene expression signatures from individual patients would replace the disease‐specific signatures.     

Modelling intravascular delivery from drug-eluting stents with biodurable coating: investigation of anisotropic vascular drug diffusivity and arterial drug distribution

In-stent restenosis occurs in coronary arteries after implantation of drug-eluting stents with non-uniform restenosis thickness distribution in the artery cross section. Knowledge of the spatio-temporal drug uptake in the arterial wall is useful for investigating restenosis growth but may often be very expensive/difficult to acquire experimentally. In this study, local delivery of a hydrophobic drug from a drug-eluting stent implanted in a coronary artery is mathematically modelled to investigate the drug release and spatio-temporal drug distribution in the arterial wall. The model integrates drug diffusion in the coating and drug diffusion with reversible binding in the arterial wall. The model is solved by the finite volume method for both high and low drug loadings relative to its solubility in the stent coating with varied isotropic–anisotropic vascular drug diffusivities. Drug release profiles in the coating are observed to depend not only on the coating drug diffusivity but also on the properties of the surrounding arterial wall. Time dependencies of the spatially averaged free- and bound-drug levels in the arterial wall on the coating and vascular drug diffusivities are discussed. Anisotropic vascular drug diffusivities result in slightly different average drug levels in the arterial wall but with very different spatial distributions. Higher circumferential vascular diffusivity results in more uniform drug loading in the upper layers and is potentially beneficial in reducing in-stent restenosis. An analytical expression is derived which can be used to determine regions in the arterial with higher free-drug concentration than bound-drug concentration.     

Effect of intravenous drug administration mode on drug distribution in a tumor slab: a finite Fourier transform analysis

Cancer therapy using chemotherapeutic drugs frequently involves injection of the drug into the body through some intravenous mode of administration, viz, continuous (drip) infusion or single/multiple bolus injection(s). An understanding of the effect of the various modes of administration upon tumor penetration of drug is essential to rational design of drug therapy. This paper investigates drug penetration into a model tumor of slab geometry (between two capillaries) in which the overall transport rate of drug is limited by intra-tumor transport characterized by an effective diffusion coefficient. Employing the method of Finite Fourier Transforms (FFT), analytical solutions have been obtained for transient drug distribution in both the plasma and the tumor following three modes of administration, viz, continuous infusion, single bolus injection and equally-spaced equal-dose multiple bolus injections, of a given amount of drug. The qualitative trends exhibited by the plasma drug distribution profiles are consistent with reported experimental studies. Two concepts, viz, the dimensionless decay constant and the plasma/tumor drug concentration trajectories, are found to be particularly useful in the rational design of drug therapy. The dimensionless decay constant provides a measure of the rate of drug decay in the plasma relative to the rate of drug diffusion into the tumor and is thus characteristic of the tumor/drug system. The magnitude of this parameter dictates the choice of drug administration mode for minimizing drug decay in the plasma while simultaneously maximizing drug transport into the tumor. The concentration trajectories provide a measure of the plasma drug concentration relative to the tumor drug concentration at various times following injection. When the tumor drug concentration exceeds the plasma drug concentration, the drug will begin to diffuse out of the tumor. Knowledge of the time at which this diffusion reversal occurs is especially useful for optimum scheduling of subsequent bolus injections in a multiple bolus dosing regimen. There are no reported applications of the FFT method to solve repeated input functions in either the chemical engineering or pharmaceutical science literature. Thus, the application of FFT method to solve multiple bolus injections is a unique one. Use of this FFT based analysis as a predictor tool can limit the number of costly experiments which are being done now to achieve this purpose. Even though the model in its present form is simplified, the analysis thereof has nevertheless led to a better understanding of the various factors that must be taken into account for rational design of drug therapy.     

Effects of cellular pharmacology on drug distribution in tissues.

The efficacy of targeted therapeutics such as immunotoxins is directly related to both the extent of distribution achievable and the degree of drug internalization by individual cells in the tissue of interest. The factors that influence the tissue distribution of such drugs include drug transport; receptor/drug binding; and cellular pharmacology, the processing and routing of the drug within cells. To examine the importance of cellular pharmacology, previously treated only superficially, we have developed a mathematical model for drug transport in tissues that includes drug and receptor internalization, recycling, and degradation, as well as drug diffusion in the extracellular space and binding to cell surface receptors. We have applied this "cellular pharmacology model" to a model drug/cell system, specifically, transferrin and the well-defined transferrin cycle in CHO cells. We compare simulation results to models with extracellular diffusion only or diffusion with binding to cell surface receptors and present a parameter sensitivity analysis. The comparison of models illustrates that inclusion of intracellular trafficking significantly increases the total transferrin concentration throughout much of the tissue while decreasing the penetration depth. Increasing receptor affinity or tissue receptor density reduces permeation of extracellular drug while increasing the peak value of the intracellular drug concentration, resulting in "internal trapping" of transferrin near the source; this could account for heterogeneity of drug distributions observed in experimental systems. Other results indicate that the degree of drug internalization is not predicted by the total drug profile. Hence, when intracellular drug is required for a therapeutic effect, the optimal treatment may not result from conditions that produce the maximal total drug distribution. Examination of models that include cellular pharmacology may help guide rational drug design and provide useful information for whole body pharmacokinetic studies.     

Recent Advances in Drug Repositioning for the Discovery of New Anticancer Drugs

Drug repositioning (also referred to as drug repurposing), the process of finding new uses of existing drugs, has been gaining popularity in recent years. The availability of several established clinical drug libraries and rapid advances in disease biology, genomics and bioinformatics has accelerated the pace of both activity-based and in silico drug repositioning. Drug repositioning has attracted particular attention from the communities engaged in anticancer drug discovery due to the combination of great demand for new anticancer drugs and the availability of a wide variety of cell- and target-based screening assays. With the successful clinical introduction of a number of non-cancer drugs for cancer treatment, drug repositioning now became a powerful alternative strategy to discover and develop novel anticancer drug candidates from the existing drug space. In this review, recent successful examples of drug repositioning for anticancer drug discovery from non-cancer drugs will be discussed.     

The Two Faces of Social Interaction Reward in Animal Models of Drug Dependence

Drug dependence is a serious health and social problem. Social factors can modify vulnerability to developing drug dependence, acting as risk factors or protective factors. Whereas stress and peer environment that encourage substance use may increase drug taking, strong attachments between family members and peer environment that do not experience drug use may protect against drug taking and, ultimately, drug dependence. The rewarding effects of drug abuse and social interaction can be evaluated using animal models. In this review we focus on evaluating social interaction reward in the conditioned place preference paradigm. We give an overview of how social interaction, if made available within the drug context, may facilitate, promote and interact with the drug’s effects. However, social interaction, if offered alternatively outside the drug context, may have pronounced protective effects against drug abuse and relapse. We also address the importance of the weight difference parameter between the social partners in determining the positive or “agonistic” versus the hostile or “antagonistic” social interaction. We conclude that understanding social interaction reward and its subsequent effects on drug reward is sorely needed for therapeutic interventions against drug dependence.     

High-throughput human metabolism and toxicity analysis

Poor drug candidate safety profiles are often identified late in the drug development process, manifesting themselves in the preclinical and clinical phases and significantly contributing to the high cost and low yield of drug discovery. As a result, new tools are needed to accelerate the assessment of drug candidate toxicity and human metabolism earlier in the drug development process, from primary drug candidate screening to lead optimization. Although high-throughput screens exist for much of the discovery phase of drug development, translating such screening techniques into platforms that can accurately mimic the human in vivo response and predict the impact of drug candidates on human toxicology has proven difficult. Nevertheless, some success has been achieved in recent years, which may ultimately yield widespread acceptance in the pharmaceutical industry.     

药材道地观与中药材生产

本文阐述了现代道地中药材的概念演变,道地中药材是经过千百年来的疗效认证而普遍得到人们认可的优质中药材。通过对中药材栽培历史的分析,本文认为道地中药材的优质性主要来自野生资源。多数中药栽培发展较晚,栽培生产技术与环境结合尚未达到最优化的程度。从道地中药材成因的角度分析,野生中药材在生长年限、种质、生境、性状、成份等各方面与栽培中药材都与明显的差异,其质量也一定不同,因此对栽培中药材的道地性的应有待于进一步认识。同时,通过将现代栽培技术与中药材栽培相结合,会对栽培中药材的质量稳定和提高发挥重要作用。     

Intrinsic or acquired drug resistance and metastasis: Are they linked phenotypes?

Evidence is reviewed which suggests a linkage may exist between certain forms of de novo or acquired drug resistance and metastasis. This includes finding that expression of certain dominantly acting mutant oncogenes or tumor suppressor genes, e.g. genes which normally act to “drive” tumor progression and metastasis, can also affect the expression of drug resistance. Moreover, this can be accompanied by altered expression of certain cellular genes thought to be involved in expression of drug resistance. A direct linkage between acquired drug resistance and metastasis would suggest that tumor sublines selected for drug resistance should manifest more aggressive malignant properties than their drug-sensitive counterparts. While this does not appear to be true for drug resistant sublines selected in vitro, indeed such cell lines frequently manifest diminished in vivo tumorigenic and/or metastatic competence, there is some evidence to support such a correlation exists for tumor cell lines that are selected in vivo for drug resistance. Attention is also drawn to the fact that new linkages between metastasis and drug resistance may be uncovered by analyzing the ability of tumor subpopulations to acquire drug resistance after one or several previous exposures to chemotherapeutic drugs, as opposed to examining intrinsic drug resistance only. Furthermore, ability to detect induced or acquired drug resistance in vitro may be strongly influenced by the types of assay used to detect and monitor drug resistanc. In particular, three-dimensional cell culture systems may reveal acquired or induced “multicellular” drug resistance in situations where conventional two-dimensional culture systems may therefore reveal as yet undiscovered associations between the phenotypes of metastasis and drug resistance.     

Overview: Evaluation of metabolism-based drug toxicity in drug development

Drug metabolism can be a key determinant of drug toxicity. A nontoxic parent drug may be biotransformed by drug metabolizing enzymes to toxic metabolites (metabolic activation). Conversely, a toxic drug may be biotransformed to nontoxic metabolites (detoxification). The approaches to evaluate metabolism-based drug toxicity include the identification of toxic metabolites and the evaluation of toxicity in metabolically competent and metabolically compromised systems. A clear understanding of the role of drug metabolism in toxicity can aid the identification of risk factors that may potentiate drug toxicity, and may provide key information for the development of safe drugs.     

Metabolism of echitamine and plumbagin in rats

When administered to rats, echitamine was absorbed rapidly from the tissues and was detected in circulation within 30 min. The drug level reached a maximum value by 2 h and then decreased steadily. The drug had completely disappeared from the blood in 6 h. The presence of echitamine was observed within 2 h in urine and the maximum amount of drug was excreted between 2 and 4 h. About 90% of the drug was excreted in urine in 24 h and the drug could not be detected in urine after 48 h. Along with echitamine, its metabolites were also detected in the urine. Plumbagin was not detected in blood upto 24 h when administered into rats. The drug was detected in urine within 4 h after administration; a major portion of the drug was excreted in urine by 24 h and traces of the drug were observed in the urine even after 48 h.     

A model predicting delivery of saquinavir in nanoparticles to human monocyte/macrophage (Mo/Mac) cells

Modeling the influence of a technology such as nanoparticle systems on drug delivery is beneficial in rational formulation design. While there are many studies showing drug delivery enhancement by nanoparticles, the literature provides little guidance regarding when nanoparticles are useful for delivery of a given drug. A model was developed predicting intracellular drug concentration in cultured cells dosed with nanoparticles. The model considered drug release from nanoparticles as well as drug and nanoparticle uptake by the cells as the key system processes. Mathematical expressions for these key processes were determined using experiments in which each process occurred in isolation. In these experiments, intracellular delivery of saquinavir, a low solubility drug dosed as a formulation of poly(ethylene oxide)-modified poly(epsilon- caprolactone) (PEO-PCL) nanoparticles, was studied in THP-1 human monocyte/macrophage (Mo/Mac) cells. The model accurately predicted the enhancement in intracellular concentration when drug was administered in nanoparticles compared to aqueous solution. This simple model highlights the importance of relative kinetics of nanoparticle uptake and drug release in determining overall enhancement of intracellular drug concentration when dosing with nanoparticles.     

NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning

Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations.     

肿瘤类器官在药物筛选和个性化用药中的研究进展*

恶性肿瘤是影响人类生命健康的重大疾病之一,药物治疗是常见的治疗手段。近年来,“精准治疗”已经成为肿瘤治疗的趋势。要实现对恶性肿瘤有效、精准的药物治疗,药物筛选模型至关重要。肿瘤类器官是近年来新兴的一种三维细胞模型,具有经长期传代还保留亲本肿瘤的特征和异质性、培养成功率高、周期短和能够高通量筛选药物等优点,已被用于药物筛选、预测患者对治疗的反应以及为个性化用药提供指导等。重点介绍了肿瘤类器官在药物筛选及个性化用药中的研究进展和面临的挑战。     

A strategy for the control of HIV infection and viral hepatitides against the background of the narcotic abuse epidemic

The presence of the epidemic of drug addiction in the Russian Federation is stated: for the recent 10 years the number of drug addicts increased 3.5-fold. The questioning of several thousand drug addicts has revealed that 60% of them have hepatitis B, 80% have hepatitis C, 10% have syphilis. The strategy of controlling HIV infection among drug addicts is proposed. It includes, in the first place, the prevention of the secondary infection from HIV-infected addicts; in the second place, the detection of HIV-infected persons in groups of drug addicts, difficult to establish contacts with; in the third place, the primary prophylaxis within the program "Harm reduction" in the most susceptible groups of drug addicts. The thesis that the prevention of HIV infection must become the prevention of drug addiction by rendering multi-stage assistance to HIV-infected persons, including mainly drug addicts, is substantiated.     

Ultrasound-induced biophysical effects in controlled drug delivery

Ultrasound is widely used in biomedical engineering and has applications in conventional diagnosis and drug delivery. Recent advances in ultrasound-induced drug delivery have been summarized previously in several reviews that have primarily focused on the fabrication of drug delivery carriers. This review discusses the mechanisms underlying ultrasound-induced drug delivery and factors affecting delivery efficiency, including the characteristics of drug delivery carriers and ultrasound parameters. Firstly, biophysical effects induced by ultrasound, namely thermal effects, cavitation effects, and acoustic radiation forces, are illustrated. Secondly, the use of these biophysical effects to enhance drug delivery by affecting drug carriers and corresponding tissues is clarified in detail. Thirdly, recent advances in ultrasound-triggered drug delivery are detailed. Safety issues and optimization strategies to improve therapeutic outcomes and reduce side effects are summarized. Finally, current progress and future directions are discussed.

     

An approach to the modeling of the tolerance mechanism in the drug effect. II: On the implications of compensatory regulation

In the previous paper (Peper et al., 1987: J. theor. Biol. 127, 413), a model of drug tolerance was developed based on the assumption that the decrease of drug effect after repeated administration of a drug is caused by the involved regulations in the organism adapting themselves to the presence of the drug. In the present paper, the behaviour of the model is studied with respect to the dose-response relation, the drug effect in dependent and non-dependent subjects and withdrawal symptoms. Computer simulations demonstrate the model to be highly sensitive to sudden changes of drug dose. Dependent on the open loop gain of the adaptive mechanism, a sudden decrease of drug dose might result in an effect opposite to the common drug effect. In the model, the rate of decrease of drug dose necessary for optimal drug withdrawal appears to be determined by the same mechanism as the rate of increase of dose necessary for a constant effect at the commencement of treatment. The behaviour of the model suggests the degree of drug dependence in an addicted subject to depend on the extent to which non-somatic factors are involved in the process of initiation of the adaptive mechanisms.