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The analyses of experimental results which are necessary in the determination of the structure and rate constants of pharmacokinetic systems are frequently subject to considerable errors. These errors, resulting from assay techniques and from physiological mechanisms within the test subject, make interpretation of results difficult. This may lead to problems in distinguishing between various feasible models and can result in a need to employ over‐simplified models for subsequent work. A technique is proposed which may help to reduce the effects of such errors. The technique is applied to simulations of experiments and results are presented which allow comparison of the accuracy of the parameter estimates with those obtained by methods which are more normally employed.

In this paper, non‐linear compartment modelling is used to study drug transport of anticancerous substance across brain tissues. The aim of the work is to identify the pharmacokinetic parameters of the model created.

A combination of the Adomian decomposition method and the Alienor reducing transformation method were used to solve the identification problem as if it were a classical one‐dimensional minimization problem.

The numerical results using this methodology have shown that local therapeutic method should be preferred, when it comes to evaluate the rate (of healthy cells/cancerous cells), especially when, somehow, the drug transition into the tumour is speeded up. The combination method of Adomian and Alienor proved a successful strategy, and could take into account many of the pharmacokinetic parameters as necessary and use well‐known algorithms.

It is believed that this modest work can be considered as preliminary steps for improving local drug administration.

The study has shown that pharmacokinetic/pharmacodynamic modelling can help to understand the drug behaviour in such complex media and hence avoid the most threatening side effects by predicting the toxicity threshold of a drug and therefore minimize the therapeutic index.

Shows the powerful tools of Adomian and Alienor techniques that can be applied successfully in biomedical applications.

The purpose of this paper is to investigate the salivary fluoride retention as fluoride concentration, amount of soluble fluoride, half-life (t_1/2) and salivary flow rate of different amounts of toothpaste and rinsing procedures.

A randomized crossover study of 21 healthy volunteers was designed to compare pharmacokinetic parameters of 1 g (B1) and 0.3 g (B0.3) of toothpaste without rinsing and brushing with 1 g of toothpaste with expectoration followed by water rinsing (B1R). Unstimulated saliva was collected before brushing as a baseline and at 0, 5, 10, 30, 60 and 90 min after the completion of the tooth brushing procedure.

The salivary fluoride concentration and amount of soluble fluoride of the B1 group were significantly higher than the B0.3 and B1R groups. The B1 and B1R groups prolonged the remineralizing level up to 60 min while the B0.3 group retained their remineralizing levels for 30 min. The initial t_1/2 (rapid phase) of B1 and B1R groups were significantly longer than the B0.3 group. The late t_1/2 (slow phase) of the B0.3 group was significantly longer than the B1 group. This is called the two-compartment open pharmacokinetics model. There was no statistical difference of salivary flow rates between all groups.

Non-rinsing and the amount of fluoride toothpaste play an important role in raising salivary fluoride levels and prolonging the remineralizing level of the oral cavity.

Drug warehouses (DWs) play a crucial role in drug distribution of government-supported healthcare supply chain as it controls both the cost and responsiveness of the logistics activities. The current study proposes a methodology using data envelopment analysis (DEA) to estimate the performance along different dimensions and was applied to 30 government-supported DWs.

This study employs DEA to evaluate the performance and relative technical efficiency of DWs. In this research, four inputs and six outputs are identified based on intensive literature review and discussion with all stakeholders of DWs. The inputs are warehouse storage capacity, temperature-controlled storage capacity, number of skilled employees and operational cost, while the outputs are fill rate, number of generic drugs, volume of drugs, consumption points, inventory turns ratio and time efficiency.

Results show that 30% DWs operate at the most productive scale size with 100% efficiency level while 47% DWs have a significant possibility for further enhancement in productive efficiency and 23% DWs should diminish their operational size to increase their productivity level. It was also found that achieving 100% operational productivity along warehouse space capacity needs significant effort, whereas other three inputs, namely temperature-controlled capacity, number of skilled employees and operational cost, require comparatively less effort. Similarly, it was observed that the performance along the fill rate and time efficiency is satisfactory, whereas the performance along other fours output variables (i.e. number of generic drugs, volume of drugs, consumption points and inventory turns ratio) needs to be improved.

The findings offer insights on the inputs and outputs that significantly contribute to efficiencies so that inefficient DWs can focus on these factors.

Although many issues related to DEA have been widely researched and reported, but no literature has been found for analysis of DWs in general and government-supported DWs specifically to find out efficiencies for supply chain performance improvement.

The purpose of this paper is to identify, analyze and classify (i.e. driving and dependence power) the government-supported health-care supply chain enablers (GHSCEs) in rural areas of India for enhancing availability and minimizing wastage of generic medicines.

A methodology is proposed using interpretive structural modeling (ISM) – fuzzy matriced impacts croises multiplication appliqueeaun classement (Fuzzy MICMAC) analysis to analyze the GHSCEs on the basis of inputs collected from various stakeholders about their driving and dependence power.

The performance measurement system, employee recognition and reward, technology adoption, training cell and inbuilt analytical tool for IT system were found to be the appropriate GHSCEs where efforts and resources should be put for enhancing availability and minimizing wastage.

The proposed approach provides a platform for the both researchers and academicians to understand the GHSCEs and their relationships. It also provides the direction to the government for optimally allocating the efforts and resources to enhance the current performance level of generic drug distribution.

Although many issues related to health-care supply chain have been widely researched and reported, no literature has been found for analysis of GHSCEs to choose the appropriate set of GHSCEs for supply chain performance improvement in general and developing country like India in specific.

A literature review revealed that government of various developing economies have put an effort on health-care supply chain through the executing critical factors (CFs) directly. Although they have attained some significant benefits in this tactic, but it was not up to satisfactory level. One of the reasons can be attributed to the fact that government/policy makers are not quantifying the impact of CFs on health-care supply chain. This paper aims to propose a methodology to quantify and estimate the impact of CFs on government-supported health-care supply chain (GHSC).

The Graph Theoretic Approach is proposed for estimating the impact and utility of CFs on an Indian GHSC. This study is also extended to scenario analysis for comparing results with different performance situation.

The results obtained from this study show that performance of Indian GHSC is satisfactory, but performance gaps exist which need to be reduced. In this research work, 12 CFs are identified under two significant categories (SCs), i.e. enablers and barriers and the intensity of enablers and barriers have been calculated to show the impact or influence of CFs on GHSC. The value of intensity shows that the role or impact of enabler category (i.e. performance measurement, employee recognition and reward, technology adoption, training cell, inbuilt analytical tool for IT system) is higher on Indian GHSC in comparison to barriers category to enhance the performance of GHSC.

The obtained numerical results are completely in specific to the Indian perspective only; hence, they cannot be generalized for other countries. Simultaneously, this study is related to government supported health-care system; hence, the selection of expert panel was crucial due to the unavailability of doctors and other stakeholders of government system.

The proposed approach is aimed at providing a procedure for evaluating the impact of CFs on HSC in general and GHSC in specific. This study is an attempt to assist government and top management of GHSC to assess the impact of CFs on GHSC and accordingly define its course of actions.

Although various issues related to the CFs have been broadly identified and analyzed, no dedicated study has been reported in the field for quantification of impacts of CFs. Furthermore, this proposed model has an ability to recognize the specific contribution of each CF and overall contribution.

This paper aims to review the healthcare supply chain (HSC) literature along various areas and to find out the gap in it.

In total, 143 research papers were reviewed during 1996-2017. A critical review was carried out in various dimensions such as research methodologies/data collection method (empirical, case study and literature review) and inquiry mode of research methodology (qualitative, quantitative and mixed), country-specific, targeted area, research aim and year of publication.

Supply chain (SC) operations, performance measurement, inventory management, lean and agile operation, and use of information technology were well studied and analyzed, however, employee and customer training, tracking and visibility of medicines, cold chain management, human resource practices, risk management and waste management are felt to be important areas but not much attention were made in this direction.

Mainly drug and vaccine SC were considered in current study of HSC while SC along healthcare equipment and machine, hospitality and drug manufacturing related papers were excluded in this study.

This literature review has recognized and analyzed various issues relevant to HSC and shows the direction for future research to develop an efficient and effective HSC.

The insight of various aspects of HSC was explored in general for better and deeper understanding of it for designing of an efficient and competent HSC. The outcomes of the study may form a basis to decide direction of future research.

The requirement of high-quality government-supported healthcare services has necessitated the significance of recognizing new management practices to enhance patient satisfaction. Hence, the purpose of this study is to address the patient's enhanced custom needs through the implementation of supply chain value stream mapping (SCVSM) in government-supported drug distribution system (DDS) for enhanced patient's satisfaction.

This study elucidates the role of one popular emerging management technique (i.e. SCVSM) in the healthcare sector by an investigative case study. The DDS in Rajasthan (India) was selected for this study. The data for this analysis were gathered in three ways (i.e. direct observation, documentary analysis and semi-structured interviews).

The outcome of this current study reveals that it is possible to apply the tool (SCVSM) to investigate the wastes in DDS to deliver the medicines at right time, right quantity and right quality. The application of SCVSM concluded that the various Kaizens (areas needed to improve) in lead time; transportation and routing should be adopted. The study further implemented kaizen on the current SCVSM and developed future SCVSM.

Although various stages and functions exist in the healthcare supply chain, the current study is focused on the distribution system of drugs. The proposed approach provides a platform for both researchers and academicians to understand the existing DDS and to implement the SCVSM approach in the healthcare environment. The results show that the proposed SCVSM model is able to identify some operational bottlenecks and wastes which interfere in DDS.

It was observed that limited literature related to lean implementation on DDS and implementation of SCVSM on the healthcare environment in general and government-supported or public in specific are available. The current study on the application of SCVSM in DDS is unique in nature and will definitely add value to the existing literature of the application of value stream mapping (VSM) on the healthcare supply chain management field.

The purpose of this study is to develop a methodology for the identification, categorization and prioritization of operational government-supported healthcare supply chain barriers (GHSCBs).

This study develops a theoretical background for identifying and segregating relevant GHSCBs and proposes a 5W2H (a Toyota production system) with fuzzy DEcision MAking Trial and Evaluation Laboratory (DEMATEL) embedded approach to quantify the causal–effect relationships among the identified operational GHSCBs.

Seven GHSCBs (i.e. uncertainty of demand management, lack of continuous improvement and learning, lack of deadline management, lack of social audit, warehousing equipment unavailability, human resource shortage and inadequate top level monitoring) were identified as significant cause group where the government, top management and decision-makers of government-supported healthcare supply chain (GHSC) have to put efforts.

The results obtained are specific to the GHSC of Indian perspective, which could be extended to global context. However, the proposed approach can be a base and provide a platform to understand and analyze the interactions among GHSCBs.

The proposed methodology will show the appropriate areas for allocating efforts and resources to mitigate the impact of GHSCBs for successful implementation of healthcare supply chain.

According to best of the authors' knowledge, this is the first study of operational barrier for GHSC in India in specific. The use of 5W2H embedded fuzzy DEMATEL approach for the development and analysis of the theoretical framework of Indian GHSCBs is unique in barrier literature.

In this study, a new neural differential grey model is proposed for the purpose of accurately excavating the evolution of real systems.

For this, the proposed model introduces a new image equation that is solved by the Runge-Kutta fourth order method, which makes it possible to optimize the sequence prediction function. The novel model can then capture the characteristics of the input data and completely excavate the system's evolution law through a learning procedure.

The new model has a broader applicability range as a result of this technique, as opposed to grey models, which have fixed structures and are sometimes over specified by too strong assumptions. For experimental purposes, the neural differential grey model is implemented on two real samples, namely: production of crude and consumption of Cameroonian petroleum products. For validation of the new model, results are compared with those obtained by competing models. It appears that the precisions of the new neural differential grey model for prediction of petroleum products consumption and production of Cameroonian crude are respectively 16 and 25% higher than competing models, both for simulation and validation samples.

This article also takes an in-depth look at the mechanics of the new model, thereby shedding light on the intrinsic differences between the new model and grey competing models.