A relevant feature of the model is that it does not require recent travel time data from other vehicles. For this reason, the proposed model may be used in intercity transport contexts in which service planning is carried out according to timetables. The proposed model has been tested with two real cases of intercity public transport routes and from the results obtained we may conclude that, in general, the average error of the predictions is around 13% compared to the observed travel time values.In road-based mass transit systems, travel time is a key factor in providing quality of service. This article proposes a method of predicting travel time for this type of transport system. This method estimates travel time by taking into account its historical behaviour, represented by historical profiles, and the current behaviour recorded on the public transport vehicle for which the prediction is to be made. The model uses the  TT) estimates are used for various key tasks. In operations’ planning, suitable time estimates are required for the preparation of timetables and to schedule services on the different routes. This type of planning is called long-term travel time planning. In operations’ monitoring, depending on the situation of the public transport vehicles and the conditions under which they operate the routes, travel time estimates are required to detect any potential significant deviations from the timetable and the scheduled services. This type of planning is called short-term TT planning. According to the standards and recommendations of public transport agencies [TT is one of the key factors when providing quality of service. Users wish to travel on the public transport network in the shortest possible time using punctual services, and to be informed in the event of alterations in order to avoid waiting [In road-based mass transit systems, travel time , prediction models based on machine learning regression (MLR), and prediction models based on state-based time-series (STS). Regardless of the theoretical foundations on which these prediction techniques are based, there are three common aspects. First, public transport routes for which TT predictions are to be made are modelled as a sequence of segments the endpoints of which may be stops or time control points. Second, the historical data used to implement the different proposals come from records obtained from the automatic vehicle location (AVL) systems and/or the automatic passenger counting (APC) systems installed in public transport vehicles. Third, short-term TT predictions for a public transport vehicle are done by taking as input the TT obtained by the vehicle on the route segments already travelled, and the data from the last vehicles to have travelled the route segments that said vehicle has not yet travelled. At this initial point of the review it should be mentioned that the works published on these prediction methods are generally lacking in their analysis of the input variables used to make TT predictions. We could, however, mention the works of Yetiskul and Senbil [TT behaviour in order to provide useful information for long-term TT planning. The former analysed TT behaviour in the Turkish city of Ankara. Using statistical variables, the authors studied how time, space and service factors affect TT. The latter was carried out in the city of Rome and consisted of an analysis based on time series designed to obtain TT behaviour patterns depending on time factors, and on how these patterns were influenced by traffic conditions. Based on this analysis, the authors developed a long-term TT prediction model based on time series. The review that follows is structured according to the theoretical model used by the proposed techniques, excluding those based on HAM because they were the initial proposals that have already been improved upon by proposals based on other more recent models.This section reviews the proposals for short-term d Senbil  and Comid Senbil , which aMLR methods is to infer the value of a dependent variable, in this case TT, by means of a mathematical function based on a set of independent variables, this function being the result of a prior learning process. The techniques used in the studies include artificial neural networks (ANN); particularly multilayer perceptron (MLP) networks; support vector machine using the radial-basis function (RBF) as the kernel regression function; k-nearest neighbours (KNN) with the Euclidean distance being the most used metric; and decision tree regression (DTR). Yu et al. [SVM, using RBF as the kernel function. In this proposal, it is interesting to note that for the TT prediction not only is TT taken into account, but also an attribute that indicates the weather conditions (sunny day or rainy day) and the time of day in relation to demand (peak or off-peak). The SVM model uses an input vector consisting of three variables—the segment, the TT in the current segment, and the last TT value for the next segment—and the output data are the estimated times for the segments not yet travelled by the vehicle. Chang et al. [KNN method. The data used are the records provided by the AVL systems. To make the prediction, the proposed model uses a set of historical data to obtain TT patterns, one for each day of the week; these patterns are called historical patterns. The vehicles that operate on the route for which TT is to be predicted communicate the times that they pass through the time control points to a central system. These are averaged, and a vector that characterises current TT for the route is obtained. To make the prediction, the historical k patterns closest to this vector are obtained using the Euclidean distance metric. Lee et al. [TT prediction framework in which the vehicle’s arrival times, obtained from its AVL systems, are stored in a historical record. These historical records are processed using the k-means and v-means clustering techniques in order to obtain different groups that represent significantly different TT behaviours. In order to make the prediction, the times provided by the last vehicle that completed or is travelling on a route for which the TT are to be predicted are used as input data. Taking these current trajectory times to classify the TT to be predicted, the cluster with the pattern that most closely approximates the current times of the vehicle is sought. The TT predictions for the following route points will be the average TT from the data records that match this cluster. Gurmu et al. [ANN model for real-time TT prediction using Global Positioning System (GPS) data. Gal et al. [DTR. The queueing theory model is based on the snapshot principle, using as input data the times that the last buses went through the stops. To implement the DTR-based method, the authors studied different techniques: random forest (RF), extremely randomised trees (ET), AdaBoost (AB), gradient tree boosting (GB) and an improved version of the gradient tree boosting technique (GBLAD), which produced the best results. Arhin and Stinson [TT (dependent variable). These factors are passengers boarding, passengers alighting, passenger load, dwell time, segment length, bus stops, signalised intersections, access approaches and mid-segment crosswalks. The TT is the result of evaluating a function based on these factors, each weighted by a regression factor. Zhang et al. [SVM. The data used in this proposal come from AVL systems. With historical records of traffic flow for different types of day and times of day, the authors constructed a training set to obtain a decision function that generates the prediction by combining current traffic data and the arrival times of the vehicles operating on the route.The aim of the models based on u et al.  proposedg et al.  proposede et al.  propose u et al.  proposesl et al.  proposes Stinson  proposesg et al.  uses a pSTS models are based on the assumption that the value of the variable to be predicted is a function of a linear or non-linear combination of the historical values of the state variable or variables. Shalaby and Farhan [KF). The objective of this proposal was to predict TT between stops (nonstop running time) and the time that the vehicle spends at the stops on the route (dwell time). The data used are the records from the AVL and APC systems. Vanajakshi et al. [KF. The data used for the prediction are the GPS data obtained manually over 10 days because the buses did not have AVL systems. Song et al. [TT between two consecutive stops on a route depends on the speed of the vehicle, which varies as it accelerates and decelerates, and by the time the vehicle is stationary because of traffic signals. This TT behaviour is predicted by an exponential smoothing function. In this proposal, predictions are made using two sets of data from two different sources: the records provided by the AVL systems and simulated Radio Frequency Identification (RFID) data. The public transport vehicles considered in this paper were buses and taxis. The authors concluded that with RFID data, better prediction results are obtained.Prediction methods that employ d Farhan  proposesi et al.  proposesg et al.  proposesMLR and STS models in combination. Chen et al. [ANN model and a KF algorithm to predict TT. This model takes as input data the most recent information on the arrival time of the vehicle at a time point on the route and the prediction provided by the ANN model with historical data to predict, using a KF algorithm, the TT between two points on the route that have not yet been reached. The data were collected by APC systems. Bai et al. [SVM model and a KF algorithm are used. The KF predictions were used to make short-term TT predictions. To do this, the prediction made by the SVM model and the TT taken by the last bus to travel along the segment of the route for which TT is to be predicted were taken as initial data. The authors compared the proposed model (SVM-KF) with four alternative models: an ANN model, a KF model, an SVM model, and a combined ANN-KF model. The results indicated that the proposed combined model, SVM-KF, performed better on the three road segments that were analysed.There are also proposals that use the n et al.  proposesi et al.  proposesAVL systems were used. The models considered in this study were HAM, KF and ANN. Of the three models analysed, the ANN prediction model produced the best results. In the context of three Indian cities , Jairam [KNN, KF and auto-regressive integrated moving average (ARIMA) prediction models. The study was conducted with the data provided by the AVL systems, analysing three routes (one in each of the cities). The models were analysed over a week and, from the results obtained, the authors concluded that the different prediction models provided similar results for route segments used exclusively by public transport vehicles. However, on segments of the route used by both public transport vehicles and private transport vehicles, the model that combined the KNN-KF techniques produced the best results. Hua et al. [ANN, SVM and Linear Regression, introducing three Forgetting Factor Functions; the aim was to develop a prediction model using actual multi-route bus arrival time data from previous stops as inputs.Other studies have compared the different prediction models. In the specific context of a bus line in Macae (Brazil), Fan and Gurmu  conducte, Jairam  analyseda et al.  comparedTT prediction methods. Considering these properties, the method proposed in this article is characterised by providing a predictive power similar to ANN. In addition, the proposed method provides information on TT behaviour that can be used for planning schedules and is more easily applicable to all routes of a transport network than methods with greater predictive power.TT prediction methods take into account historical TT behaviour, modelled using different techniques such as ANN, SVM, STS, etc., and current behaviour, usually represented by the time taken by the vehicle to travel the last segment it has completed. The proposed method is based on the idea that it is possible to predict short-term TT using the representative elements from a classification process to represent historical behaviour, and the TT observed at all points of interest through which the vehicle has already passed to represent current TT behaviour .For the representative elements obtained through a clustering process to reflect historical TT in a context of regular passenger road transport planned by timetable. As already mentioned above, this type of TT prediction is executed and is valid during the trip made by a public transport bus as it operates a particular route. In this section, entities related to the proposed method are presented and formalised. The objective of the proposed method is to estimate short-term L represents a generic line and a specific line is specified by means of the notation cL, where the subscript c is an integer value that uniquely identifies the line. Trips on cL are specified by means of the notation cE, where c is the identifier of the line. In the model, time is specified by the notations T and t. T represents a time interval and t represents a moment of time, which is the minimum unit of time. All trips by line cL that have been made over a period of time T are specified by the notation c,TE. Similarly, a trip that has begun at a moment of time t is specified by means of the notation c,te. Stops on the route of cL are represented by the notation cS. In the context of this study, stops on a route for which the TT is to be predicted are called points of interest and are represented by cP. Points of interest on the route of cL are designated by the notation c,iP, where the subscript i identifies the point of interest and its value matches the order in which the bus passes them following the planned route. For example, Pc,1 is the first point of interest through which the vehicles pass when operating route c. When selecting points of interest, the only restriction is that the first stop of the route cannot be a point of interest. The section of the route that runs from one point of interest to the next is called a route segment , which corresponds to the TT observed on trip c,te.Clustering methods are classification techniques that group large sets of elements, characterised by a set of attributes, using similarity criteria. For the purposes of this study, clustering techniques have two interesting properties. The first is that they do not require prior learning and the second is that there are metrics that measure the quality of the resulting clusters. In the proposed model, assuming the formulation stated above, given a significant period of time TT behaviour profiles are obtained by the k-medoids method [k-medoids technique responds well in the event of outliers. The most used distance metrics in the k-medoids technique are the Euclidean distance and Manhattan distance metrics. Equations (2) and (3) express the Euclidean and Manhattan distances, between two objects, iX and jX, for a set of Q objects, each object being represented by n attributes. Equation (2) is the Euclidean distance and Equation (3) the Manhattan distance.In the proposed methodology, the historical s method . This mek-medoids technique is shown in TT, measured in seconds, at the points of interest of a route. Each grey curve represents the TT recorded for a trip, c,tOPT. The dataset c,TOPT is formed by all the c,tOPT curves. The red, blue and green curves represent the medoids, which are the representative object of each of the three resulting clusters.An example of clustering using the To evaluate the validity of a clustering solution there are different criteria, which may be classified into three categories: external indices, which measure the extent to which cluster labels match externally-supplied class labels; internal indices, which measure the intrinsic information of each dataset; and relative indices, which are used to compare several different clustering solutions. For the purposes of this study, an internal index was chosen to measure the quality of the clusters: the silhouette function . This meiA is the average distance from object i to the other objects within the cluster and iB is the smallest average distance from i to all the objects of each of the clusters to which i does not belong. The values returned by the silhouette function are in the range −1 and 1. A value close to 1 indicates a high degree of consistency in the resulting clusters and, conversely, a value close to −1 indicates that the resulting clusters have little consistency.In expression (4), iP of the sequence of points of interest on route cL, the objective of the proposed method is to estimate the TT required to reach the next point in the sequence of points of interest of the route, i.e., the TT to reach point i+P1. The recent history for that vehicle, located at point of interest iP, is represented by a set of ordered values that represent the TT observed when going through the points of interest already travelled, represented by the i-tuple . Past history, in period T, is represented by the medoids resulting from applying the k-medoid clustering technique to the set c,TOPT, these medoids represent the historical profiles of the TT. When the vehicle is at iP, the TT to reach point i+P1 is estimated in two steps. In the first step, from the observed TT  the medoid that has the most similar behaviour to the TT behaviour recorded up to point iP is selected. The similarity metric used is the same as that used in the clustering process through which the medoids were obtained. Once the most similar medoid has been selected, then the prediction of the TT for segment iS, i.e., to reach point i+P1, is:Taking the concept of point of interest, if we assume that a vehicle is at point i+PTT1 represents the prediction of the TT taken to reach point i+P1. iTT is the TT observed at point iP and kM is the medoid to which the recorded TT are most similar. k,iM and k,i+M1 represent the attributes i and i + 1 of this medoid, i.e., the TT that represent the historical behaviours at points iP and j+P1 of the k cluster.In Equation (5), TT prediction algorithm is described for trip c,te that is completed in vehicle V.Next, the short-term K: number of clusters used to represent historical TT behaviour;M1, …, kM}: set of k-medoids that represent TT behaviour on trips on route cL;{i: last control point through which V has passed on trip c,te;TTi1→: observed arrival time at interest points 1, 2, …, i on trip c,te (recent behaviour);Mk,1→i: for the k-medoid, values of the arrival time at interest points 1, 2, …, i ;i+PTT1: objective of the algorithm, which is to predict TT at interest point i + 1.Data:TT behaviour up to the last interest point that V has passed on trip c,te. Dist is the function that evaluates the distance metric that is used to assess similarity.Step 1. Obtain the medoid that most resembles the observed minD = ∞j = 1 to K) doFor   D < minD)  If , D and D contain the values resulting from calculating this distance between TT and M1, M2 y M3, respectively. At point of interest P1, we calculated the distance between the TT observed at this point, which was 180, and the values of the medoids at this point, which were 360 for M1, 240 for M2 and 240 for M3. The Manhattan distance values obtained were 180 for D, 60 for D and 60 for D. Since the minimum distance value obtained was 60 and it was obtained with two medoids, M2 and M3, the first medoid that produced this minimum value, in this case M2, was selected for this method. The predicted TT values at each point iP are displayed in row PTT. The TT prediction to reach point of interest P2 will be made taking medoid M2 as the historical reference and the prediction will be calculated, according to Equation (5), by adding to the TT observed at P1, which is 180 s, the difference between the TT value of M2 at point P2, which is 780 s, and the value of M2 at point P1, which is 240 s. Therefore, the value of the TT prediction to reach P2 is 720 s. The TT prediction to reach point P3 would be carried out analogously to this, but in this case the TT profile on the trip will consist of two values  and the historical TT behaviour profiles will be  according to M1,  according to M2 and  according to M3. At this point the historical profile that is most similar to the profile observed on the trip is that represented by medoid M3. As shown in M3 is the medoid with a profile most similar to the observed TT values and therefore its TT values will be those used to make the predictions at the other points.The prediction method is illustrated graphically in The main objectives of the tests were to select the most suitable parameters for the proposed method, to evaluate its prediction accuracy and to compare it with other methods. To make this analysis more complete, data were collected from two bus lines with different characteristics in terms of demand, route length, types of road and the population centres that they run through. In terms of resources and tools, a computer with an Intel(R) Core (TM) i7-2600K CPU processor @ 3.40GHz with 16 GB RAM was used. Oracle DB—the database environment used by the transport operator that provided its data—was used to prepare the data. For the modelling phase, the RStudio framework was used, specifically Cluster Package  and NeurT is the year 2015. These two lines, identified by codes L1 and L303, cover two important transport corridors on the Island. Line L1 covers the Capital-South corridor, starting in the city of Las Palmas de Gran Canaria, which, as the most populated urban nucleus where the main public service centres of the island are based, is the main transport generation point. The line goes through the main tourist centres—tourism is the largest sector of the island economy and generates the most employment. Line L303 covers the Capital-Centre corridor, starting in the city of Las Palmas de Gran Canaria and travelling through the largest population centres in the centre of the island. L1, of the 91 stops, 7 points of interest were selected. In the case of line L303, six were selected, giving rise to five route segments. Two aspects were taken into account when selecting stops as points of interest: the number of travellers and the distance between the stops (stops very close to each other were ruled out). The routes taken by the two lines travel along different types of road  and, due to the different motivations and type of user, moments of peak and off-peak demand vary. The datasets used came from the operational records of two lines operated by the intercity public transport network of the Island of Gran Canaria. The period of time analysed in the study was the whole of 2015. Therefore, according to the model, the value of GPS location data that each bus automatically records every minute. In order to obtain all the TT for the trips completed in 2015, a data mining process was carried out to reconstruct all the trips on the routes to be analysed. NGPS), the number of reconstructed trips (NRE) and the number of these trips for which the routes are consistent with the planned route and do not have errors (NCRE). The set of correct trips is dataset E1,2015 in the case of L1, and E303,2015 for L303.The basic data unit used was the OPT1,2015 and OPT303,2015 datasets, specifying different numbers of clusters beforehand, and evaluating the quality of the resulting segments with the silhouette function. L1. However, it may be observed in the case of route L303 that the function produces higher values, that is, higher levels of consistency in the clusters, when the Manhattan distance is used. Regarding the number of clusters, the best levels of consistency, regardless of the route analysed and the metric used, occur when two clusters are used. Indeed, both for route L1 and for route L303, the values of the silhouette function are very close to 0.45 using both the Euclidean metric and the Manhattan metric. It may also be observed that, irrespective of the route analysed and the distance metric used, as we increase the number of clusters used in the clustering process, the value of the silhouette function decreases, which means that the degree of consistency decreases. L1 and L303 using two clusters. The objects of the clusters are drawn in grey and the medoids of each cluster are drawn in red and blue. These medoids are the ones used in TT predictions that are displayed as results.The first phase of the tests was designed to check how the distance metric and the number of clusters affected the process of creating the clusters. Two distance metrics were tested: the Euclidean and the Manhattan. These metrics were used to generate different segments in the MAPE), which is expressed according to Equation (6). In this equation, N represents the number of points of interest on the route, i varies between 1 and the total number of segments. iOPT is the TT recorded for segment i and iPTT is the predicted TT for segment i. Both OiPT and iPTT are expressed in seconds. For this evaluation, all OPT1,2015 and OPT303,2015 data records were used for the samples.The metric used to evaluate prediction accuracy for a trip was the mean absolute percentage error  was obtained: on this segment the percentage of error in TT prediction in relation to the observed value was 15.49%. The best prediction was made for segment S4, where an error percentage of 7.61% was obtained. The average value, considering the MAPE values for all segments of the route—MAPEAV row—was 0.1106. Therefore, considering the MAPE values for all the segments, PSM commits an error of, on average, 11.06% of the observed TT values. With regard to L303, S2 was the segment for which the highest MAPE value (0.223) was obtained: on this segment the percentage of error in the prediction, in relation to the observed value, was 22.49%. The best prediction was made for S4, where the lowest MAPE value (0.0939) was obtained: an error percentage of 9.39% in relation to the observed TT values on that segment. The average value, considering the MAPE values for all segments of the route—MAPEAV row—was 0.1325, i.e., the average error rate was 13.25% in relation to the TT observed for all segments. PSM was compared with two reference methods that are widely used in the proposals described in the section on short-term TT prediction methods. These alternative methods are: predictions based on the average TT obtained from the observed TT (AvgTT) and an ANN prediction model. The analysis consisted of comparing the MAPE values obtained from these alternative methods with the values provided by the proposed method. The resulting MAPE values were compared on each segment of the analysed routes as well as the global value obtained for all the routes. The required processing times of each method were also compared.ANN model used for the comparison was the MLP network, analogous to that used in [i that the vehicle has arrived at, and the TT observed since the beginning of the route. The output variable of the network was the TT of the vehicle in segment i. The dataset used for training the two ANNs, one for each route, was built with 80% of the data records from the OPT1,2015 and OPT303,2015 datasets and the test dataset used the remaining 20%. Based on the results of the tests, using three, four and five nodes in the hidden layer, the configuration with three nodes was chosen as the configuration with the best results.The  used in . The inpL1 clearly indicate that AvgTT produces the worst results. PSM provided better results for S1 and S5 compared to the ANN and AvgTT methods, obtaining a considerably better prediction for S5 segment. ANN produced better results for S3 and S6. On S2, the MAPE values obtained by the PSM and ANN methods were practically the same. If the MAPE values obtained for all the segments are taken into account, the conclusion drawn is that PSM produces values that vary less than those produced by AvgTT and ANN. PSM performed the worst on S1, with an MAPE value close to 0.15. The best PSM performance was obtained on S4, with a value close to 0.07. In the case of ANN, it performed best on S6, with a value close to 0.06, and worst on 5S, with a value close to 0.32, which even exceeded the value obtained for that segment by AvgTT. The results obtained for route L303 are shown in AvgTT provides the worst results. PSM produced better results than AvgTT and ANN on segments S2 and S3. ANN produced better results on segments S1 and S4. As was the case with line L1, if the MAPE values obtained for all the sections are taken into account, PSM produces values that vary less than those produced by AvgTT and ANN. It can be seen that the worst performance of PSM was on segment S2, with a MAPE value close to 0.22. For the rest of the segments, the values obtained were similar and close to 0.10. In the case of ANN, the best performance was on segment S4, with a value close to 0.05, and the worst on S2, with a value close to 0.27, very close to the value obtained for that segment by AvgTT.MAPE values obtained for each segment of each of the routes analysed. It can be seen that PSM performed the best since it provided a value close to 0.11 on L1 and 0.13 on L303. In the case of ANN, the average MAPE values obtained for the two routes were similar, and close to 0.14. The worst-performing method was clearly AvgTT with values of 0.21 and 0.29 for L1 and L303, respectively.PSM and ANN in all cases, certain aspects that make PSM more attractive need mentioning. First, it is not necessary to normalise the data, as is the case of ANN where this needs to be done to. On the one hand, avoid differences between the magnitudes of the variables that affect the calculation of the weightings during the training phase. On the other hand, adapt them to the corresponding ranges of the different activation functions. Second, there are considerable differences between the time used to create the TT profiles used by PSM, and the time spent on the ANN training process. For example, on one of the analysed routes the training of the network took 59 times longer than the clustering process, as can be seen in PSM model. Fourth and lastly, ANN errors have greater variability, as can be seen especially in the case of line L1 (Although substantial differences were not observed between the results obtained by  line L1 a.PSM provides a prediction accuracy similar to that provided by ANN. However, PSM is more stable behaviour in the accuracy of its predictions. In addition, the behaviour patterns represented by the medoids used in PSM provide interpretable information about TT, an advantage that ANN and other models with high predictive power—described in SVM—do not have. Considering another property shown in PSM is easier to apply, with lower computational cost, than models based on ANN. In this regard, the computational cost of applying PSM to TT predictions on all routes of a transport network is considerably lower than that of applying techniques based on ANN. This is because PSM does not require training processes for its application, whereas ANN-based prediction systems do require learning, which would also have to be done for each route of the transport network. Given the characteristics of the SVM-based methods—another method with high predictive power—PSM would also be easier to apply to all the routes of a transport network, with a lower computational cost.Considering the results, it can be said that ANN-based methods, the predictions made by PSM in situations involving exceptional conditions, such as for example abnormally slow speed of the vehicle due to traffic incidents, have a lower accuracy. For this reason, the combined prediction methods ANN-KFM or SVM-KF were used. Therefore, it may be inferred that the use of a combined PSM-KF model could be used in contexts in which recent historical information provided by other vehicles was available, as in the case of urban transport. Finally, we would like to underscore the fact that the low computational cost of making TT predictions with PSM would enable predictions to be calculated on the vehicle itself.As with the TT prediction model for public transport buses is proposed. To estimate TT, the model takes into account the historical behaviour of TT, represented by the medoids resulting from a clustering process based on the k-medoids technique, and the current TT behaviour, which is represented by the TT observed on the vehicle for which the prediction is made. Therefore, the method does not require recent TT data provided by other vehicles, meaning that it can be executed autonomously on the vehicles themselves. The method has been applied in two real cases of public transport lines of different characteristics. The results show that, in general, the average error made in the predictions is around 13% of the observed TT values, a result similar to that obtained by the alternative method ANN, although its variability is less than this alternative method. The proposed method has some interesting features. The first is that, since it does not need recent TT data provided by other vehicles, the method can be used in the context of line services planned by timetable, which is the planning method used for intercity transport. Second, the method not only provides short-term TT estimates; by using the k-medoids clustering technique to extract TT patterns from bus trips, it also provides information on its behaviour, thus helping to make long-term TT predictions, which are used in the planning of line services. Both from the computational point of view and the configuration of the required parameters, the method is less costly than the most widely used alternative methods, based on machine learning regression or state-based time-series, because it does not require costly learning processes and validation. Therefore, the prediction of short-term TT on all the routes of a transport network can be carried out by the proposed method at considerably lower cost.In this article, a short-term


The central nervous system (CNS) may be considered as a sanctuary site, protected from systemic chemotherapy by the meninges, the cerebrospinal fluid (CSF) and the blood-brain barrier (BBB). Consequently, parenchymal and CSF exposure of most antineoplastic agents following intravenous (IV) administration is lower than systemic exposure. In this review, we describe the different strategies developed to improve delivery of antineoplastic agents into the brain in primary and metastatic CNS tumors. We observed that several methods, such as BBB disruption (BBBD), intra-arterial (IA) and intracavitary chemotherapy, are not routinely used because of their invasiveness and potentially serious adverse effects. Conversely, intrathecal (IT) chemotherapy has been safely and widely practiced in the treatment of pediatric primary and metastatic tumors, replacing the neurotoxic cranial irradiation for the treatment of childhood lymphoma and acute lymphoblastic leukemia (ALL). IT chemotherapy may be achieved through lumbar puncture (LP) or across the Ommaya intraventricular reservoir, which are both described in this review. Additionally, we overviewed pharmacokinetics and toxic aspects of the main IT antineoplastic drugs employed for primary or metastatic childhood CNS tumors , with a concise focus on new and less used IT antineoplastic agents. Childhood cancers include many malignancies that also occur in adults, such as acute lymphoblastic leukemia (ALL) which is by far the most common, followed by central nervous system (CNS) tumors, lymphomas and certain bone cancers (osteosarcoma and Ewing sarcoma). Cancers that are exclusive to children include neuroblastoma, Wilms tumor, rhabdomyosarcoma, and retinoblastoma. Improvements in the treatment of the childhood cancers have positively influenced overall disease-free survival, such as in ALL . “CNS pr“CNS prophylaxis” for pediatric ALL derived from the recognition of CNS as a sanctuary site protected from the systemic chemotherapy by the meninges, the cerebrospinal fluid (CSF) and the blood-brain barrier (BBB) ,5. For most antineoplastic agents, the total CSF exposure following administration of a systemic dose is less than 10% of the systemic exposure, as shown in In this review, we summarize the principal aspects of physical and biochemical protection of the brain, describing the several alternative strategies experimented during time to improve brain delivery of chemotherapy in primary or metastatic childhood CNS tumors , such as BBB disruption, intra-arterial (IA), intracavitary and IT chemotherapy. Moreover, we provided an overview of pharmacokinetics and toxic aspects of IT antineoplastic agents commonly used in pediatric oncology for primary or metastatic CNS tumors.The human brain is soft and physically protected by bones of the skull, the meninges and the CSF. The meninges present three layers in children and adults: the dura mater, the arachnoid and the pia mater . The dur+ and urea, but is higher in Na+, Mg++ and Cl− concentration. The mean CSF volume is 150 mL in adults, with 25 mL in the ventricles and 125 mL in subarachnoid spaces, but with marked interindividual variations [The CSF is a clear liquid, similar in composition to blood plasma, without blood cells and with less protein and different ion concentration than plasma. In fact, CSF is composed of water for 99%, compared to the 92% of plasma. It is lower than plasma in protein, Kriations . CFS serves two purposes: physical protection of the neuraxis and a chemical role in the homeostasis of cerebral interstitial fluid and neuronal environment. The buoyancy of the CSF reduces the weight of the brain, preventing the brain from crushing under its weight. It forms a liquid cushion that assures hydro-mechanical protection of the CNS from blows and trauma. CSF also plays a prominent role in brain development, by regulation of the brain interstitial fluid homeostasis, the electrolyte balance, the circulation of active molecules and elimination of catabolites .CSF secretion in adult varies from 400 to 600 mL/day, at approximately 25 mL/h. It is predominantly, but not exclusively, secreted by the choroid plexuses inside the ventricles, under neuroendocrine and hormonal modulation. A minimal poorly defined role in the CSF secretion is also played by the extrachoroidal secretion, derived from the ependymal epithelium, from the extracellular fluid and the cerebral capillaries across the blood-brain barrier ,13. CSF circulates over the ventricles, with a pulsatile and unidirectional flow along subarachnoid spaces through three foramina: the lateral ventricles, the third and fourth ventricles and the subarachnoid space. From the fourth ventricle, it enters the subarachnoid space between the arachnoid and pia mater. CSF is reabsorbed mainly into venous blood across cranial and spinal arachnoid villi and minimally via paraneural sheaths of nerves into lymphatics. CSF is renewed four to five times per 24 h in young adults. The reduction of the CSF turnover rate during ageing leads to accumulation of catabolites in the brain and CSF observed in certain neurodegenerative diseases. CSF pressure varies according to the age, from 3-4 mmHg in infants to 10–15 mmHg in adults, depending from a dynamic equilibrium between CSF secretion and absorption, influenced by systolic pulse wave, respiratory cycle, abdominal pressure, jugular venous pressure, state of arousal, physical effort and posture . The ana2. The vasculature of central nervous system supplies the brain of nutrients and oxygen, but the presence of a protective BBB between the blood compartment and the brain is an essential prerequisite to assure correct neuronal functioning of the brain [The brain is the best-perfused organ in the body, with over 100 billion capillaries, a total length of 400 miles and a total surface area of 20 mhe brain .The BBB develops during fetal life, but it is not yet entirely formed and shows unique properties not present in the adult type . Still, In the developed brain the areas of the brain without BBB are known as “circumventricular organs”, including pineal body, subfornical organ, median eminence, neurohypophysis, area postrema, vascular organ of the lamina terminalis . The BBB provides a protective mechanism that helps to maintain a stable environment for the brain, by highly selective regulation of the exchange of ions, nutrients, metabolites and potentially toxic substances between blood and the brain . The BBB is made by the endothelial cells of the brain capillaries, that are in continuous interactions with astrocytes, pericytes and perivascular macrophages, forming the “neurovascular unit” (NVU) . In braiThe passage of drugs through the BBB is influenced by several conditions: molecular weight (large molecules do not easily pass through the BBB), ionization at physiological pH , liposolubility (low lipid-soluble molecules do not simply penetrate into the brain) and protein binding . Furthermore, the achievement of therapeutic concentration of drugs in CNS is complicated by the presence of ATP-Binding Cassette (ABC) efflux transporters, a distinct set of efflux protein localized on the BBB. These efflux transporters are neuroprotective, because they limit the brain entry of neurotoxins; however, they could also restrict the entry of many therapeutics, contributing to CNS pharmacoresistance ,27.In humans, P-gp is the best known multidrug resistance (MDR) transmembrane protein, involved in ATP-dependent drug efflux pump, extruding several potentially toxic substances such as various anticancer drugs ,29. In aNumerous CNS pathologies  involve BBB dysfunction, which can range from mild and transient to chronic barrier breakdown . The structure of tumor blood vessels in aggressive brain tumors (such as glioblastoma multiform) is very different from normal blood vessels, including a high endothelial proliferation with widespread infiltration of surrounding tissue. The loss of tight junction in the tumor endothelium leads to the disruption of BBB in the primary tumor site, but not in the infiltrative tumor areas, that are consequently the most difficult lesions to treat. Treatment of primary tumors may be facilitated by the permeable vessels, but not in the infiltrative tumor areas, were the BBB is provided of tight junctions ,37. Besides, the tumor vascular system maintains the other biological components of the BBB, such as multidrug resistance-associated proteins. Consequently, many anti-cancer drugs are large, hydrophobic and unable to easily go through the BBB, but they are also substrates for the MRD efflux pumps also expressed by tumor vessels . Over the years, various strategies have been developed to improve drug delivery into the brain, bypassing the BBB . AlthougIn 1972 Rapaport et al. suggested for the first time that intra-arterial administration of hyperosmotic solutions  may produce temporary shrinkage of endothelial cells, with consequently opening of tight junctions for several hours, allowing in this time window the delivery of chemotherapeutical agents . BBBD seems to be temporary and reversible. Moreover, the non-selective opening of the BBB allows an uncontrolled influx of low and high molecules  into the brain, with a high risk of seizures and reversible aphasia or hemiparesis .The clinical benefit of osmotic BBBD has not been established for the treatment of less sensitive tumors like gliomas, whereas it seems to increase survival in patients affected by primary lymphoma and other chemo-sensitive tumors . In theiOsmotic BBBD has a more limited practice in children than in adult. In 2006 Hall et al. conducted a study among eight patients (median age 11 years) with diffuse pontine glioma (DPG), treated with monthly osmotic BBBD chemotherapy with a low toxicity profile and survival times longer than those previously reported in other DPG series, concluding that further examination of this treatment for pediatric patients with DPG or other malignant brain tumors was necessary .2 receptors expressed on the endothelial cells. Lobradimil increases permeability of the BBB within minutes following the infusion, by separating the tight junctions between the endothelial cells of the brain capillaries, with the restoration of BBB within 20 to 60 min from the end of the infusion [An alternative attempt to osmotic BBBD is based on the use of an intra-carotid arterial infusion of bradykinin which stimulates receptors on the BBB, inducing opening of tight junctions via second messengers . RMP-7 , hearing loss (up to 15% with cisplatin), seizures, confusion and neurocognitive deficits. The risk of these toxicity seems to be related to the heterogeneous distribution of the drug through the various branches of the carotid artery, while the incidence of neurological side effects is significantly improved since the use of novel selective micro-catheters .IA delivery of chemotherapy is well-established for the treatment of hepatocellular carcinoma and retinoblastoma ,52. Overa  equation defines the pharmacologic advantage that a drug may have when administered IA versus the IV route:Not all drugs have the appropriate metabolism and pharmacokinetic profile for IA usage. The Ra is maximized by a rapid total body clearance of the infused drug, which allows, after the “first pass” (during the venous recirculation phase), to extract by the tumor tissue a relative smaller amount of drug than the amount extracted during the “first pass” [Rst pass” . The drust pass” . Howeverst pass” . Another strategy for circumventing the BBB is the intracavitary chemotherapy (IC), which consists in the implantation of a biodegradable polymer wafer, chemotherapy-impregnated, into the brain or tumor cavity at the time of surgery. This procedure allows obviating these problems, providing the local sustained release of chemotherapeutic agents to the tumor site, prolonging local exposure with minimal systemic toxicity. Gliadel wafers are biodegradable polymer loaded with BCNU (carmustine), which has considerable systemic toxicity and a short half-life in serum . GliadelIn their study, Sardi et al. reported the cases of three pediatric patients with recurrent anaplastic brain ependymoma, first treated with surgery, then followed by of intracavitary BCNU wafers (Gliadel) implantation in combination with low-doses oral etoposide. Their approach was not effective for the treatment of refractory anaplastic ependymoma. Further studies are necessary to define the potential role of BCNU wafer implantation in the tumor bed after the first tumor resection in addition to consolidated systemic chemotherapy in children with high-risk brain tumors .The administration of antineoplastic drugs directly in the CSF allows to bypass the selective filter of BBB, achieving significant concentrations of the antineoplastic agents in CSF, while reducing the likelihood of systemic toxicity . In factThe Ommaya reservoir is a subcutaneous device, with a catheter inserted in one of the lateral ventricles of the brain, providing direct access to ventricular CSF . Drugs aThis technique is indicated for patients that required prolonged treatments and when LP could be difficult to perform. Additionally, the advantage of this approach is a more homogeneous drug distribution and a higher drug concentration in the subarachnoid space compared with LP. Better clinical results are achieved especially for the treatment of CNS leukemia or of neoplastic meningitis due to solid tumors . ConversMoreover, the implantation of Ommaya reservoirs may cause long-term complications, including catheter obstruction, hemorrhage and infections. Peyrl et al. analyzed their 20-year experience with Ommaya reservoirs in 98 children with brain tumors, concluding that complications may be reduced with specific training to all workers involved in implanting and accessing the device, with careful attention to strict aseptic conditions . Gerber LP is an invasive technique commonly used in pediatric oncology as a diagnostic procedure, but also therapeutically, to inject medications directly into the subarachnoid space . PatientRepeated LP are stressful and painful; therefore, pharmacological sedation is recommended in pediatric patients. Maurizi et al. conducted 252 lumbar punctures under deep sedation with propofol and ketamine in 25 children with ALL, achieving satisfactory sedation and analgesia care in all patients, reducing the risk of traumatic LP and improving comfort and quality of life (QoL) . After drug injection, it is recommended to stay at least 1 h supine, flat on the back on a 15–30 degree incline with the feet elevated above the head (Trendelenburg position), to facilitate the penetration of the drug in the brain ventricles, avoiding its prolonged presence in the spinal cord and consequently its absorption through the venous vertebral plexus into the blood. In fact, the slow and unidirectional CSF flow limits the distribution of the drug, which results in relatively low and variable concentration in the ventricular CSF. Studies in animals have shown that ventricular levels of MTX at 1 h after intralumbar injection were as much as 1000 higher in animals kept prone or in Trendelenburg position, compared with CSF concentration in animals kept upright . LP may determine several complications, such as post-puncture headache, lower back pain, nerve root irritation, infections or hemorrhages. Another disadvantage consists in the potential injection or leaking of the drug into the subdural or epidural space, which seems to occur in about 10% of LP. Additionally, delayed clearance of intrathecal chemotherapy may be observed in a lot of conditions that interfere with CSF flow. In contrast, the patients with a ventriculo-peritoneal or ventriculo-atrial shunt are at increased risk of a rapid clearance of the drug from the CSF space by the shunt, with an inadequate drug exposure .Despite the presence of numerous systemically active drugs, only a few chemotherapeutic agents are currently available for IT delivery in pediatric oncology, such as MTX, cytosine arabinoside (Ara-C), corticosteroids, and thiotepa. More recently, some other antineoplastic agents are under evaluation for IT administration. In There are several antineoplastic drugs limited to systemic administration, such as melphalan, chlorambucil, cisplatin, mitoxantrone, dactinomycin, mercaptopurine and vincristine . Their IAs shown in IT MTX has been used for over 50 years in pediatric oncology for the prevention and treatment of CNS blast cell infiltration in acute leukemias and lymphomas, either as a single agent or in combination with Ara-C and hydrocortisone, in the so called “triple intrathecal chemotherapy” . 1/2) of 4.5 h and a final t1/2 of 14 h. MTX clearance is provided by CSF reabsorption, thus it mainly depends from CSF flow. In fact, MTX slowly diffuses from CSF to the plasma compartment, therefore closely repeated IT infusions may behave as a prolonged IV infusions, causing systemic toxicity [The pharmacokinetics of IT MTX is different from systemic administration. Elimination of MTX from the CSF is biphasic, with an initial elimination half-life . This “concentration times time (C × T) approach” increases the duration of exposure of tumor cells to antineoplastic drug and avoids excessive peak concentrations, simultaneously increasing the potential efficacy and decreasing toxicity ,88. The risk of neurotoxicity from IT MTX is augmented in the presence of obstructive hydrocephalus, meningeal leukemia or leptomeningeal spread of solid tumors which may interfere with MTX CSF clearance . PotentiRarely patients may develop a sub-acute toxicity within 2 or 3 weeks of MTX IT injection, probably due to a persistent toxic drug concentration in the CSF, with the onset of reversible or irreversible paraplegia, myelopathy or encephalopathy, characterized by limb weakness, ataxia, cranial nerve palsies, visual impairment, seizures or coma . A late and chronic neurotoxicity may occur months or even years after MTX IT treatment, presenting with progressive demyelating leukoencephalopathy, with limb spasticity, dementia or coma. The addiction of cranio-spinal irradiation or the simultaneous repeated administration of high-dose IV MTX seem to enhance the risk of chronic MTX neurotoxicity .2, which hydrolyzes MTX in an inactive metabolite [Finally, an accidental MTX IT overdose is associated with an acute life-threatening toxicity, which requires immediate rescue. Different approaches may be useful if promptly applied, such as CSF drainage, ventricololumbar perfusion, systemic administration of high-dose leucovorin and steroids, intrathecal instillations of carboxypeptidase-Gtabolite ,94,95,96IT Ara-C is another effective agent primarily used in the treatment of CNS leukemia and lymphoma for the prevention and treatment of CNS infiltration, alone or in TIT chemotherapy. As MTX IT, the dosage of Ara-C IT is calculated according to age rather than BSA .1/2 of 1 h and 3.4 h. Following a single IT Ara-C dose of 30 mg, a peak CSF concentration >2 μM/L is reached and it remains above 1 μM/L during at least 24 h. Zimm et al., conducted a pharmacokinetic study in seven patients with leukemic meningitis in complete remission, showing that a single dose of 30 mg of IT Ara-C may produces CSF concentration of 1 μM/L, providing a 24-h coverage. As with intraventricular MTX, the “C × T approach” may prolong the exposure to the Ara-C. Pharmacokinetics studies have demonstrated that daily intraventricular administration of 30 mg of Ara-C for 3 consecutive days allows to maintain a cytotoxic concentration for 72 h, compared to about 24 h after a single 70 mg dose [Elimination of Ara-C from CSF is biphasic, with t mg dose .IV Ara-C is rapidly eliminated from plasma through the enzyme cytidine deaminase, which metabolize Ara-C into the inactive uracil arabinoside (Ara-U). Conversely, the conversion to Ara-U is negligible after IT administration, because of the negligible concentration of the enzyme in CSF . Thus, tAfter systemic administration of low dose Ara-C such as 100 mg/mq) not significant CSF concentrations may be observed, but the use of high IV doses of Ara-C (such as >1 g/mq) produces a CSF level of Ara-C above 1 μM/L, with increased risk of unexpected neurotoxicity. The most observed neurotoxicity after Ara-C IT is a chemical aracnoiditis, other common ones are seizures, transient paraplegia, myelopathy and encephalopathy  mg/mq no.1/2 was increased approximately 40-fold versus conventional Ara-C (from 3.4 to about 141 h), ensuring cytotoxic concentration of about 14 days with a single dose of 50 mg of IT DepoCyt [DTC 101 (DepoCyt) is a slow-release formulation developed to allow a prolonged exposure to cytotoxic concentration, with a lower frequency of IT Ara-C administration. Ara-C is encapsulated in multivescicular liposomes called DepoFoam, that gradually degrade, entering in the normal lipidic pathway of the body . After a DepoCyt . In a phase I trial of IT DepoCyt in children with advanced meningeal malignancies, Bomgaars et al. found a maximum-tolerated dose of 35 mg, when administered with dexamethasone with the evidence of prolonged disease stabilization or response . The toxicity profile of DepoCyte is similar to the conventional Ara-C. Chamberlain et al. retrospectively analyzed the incidence of neurotoxicity after administration of DepoCyt in adult patients with leptomeningeal metastasis, demonstrating that DepoCyt was generally well tolerated, whereas serious treatment-related neurological complications  occurred in about 12.5% of patients . FurtherThe use of hydrocortisone in combination with MTX and Ara-C in TIT chemotherapy has been showed to have an additive and synergic effect for prophylaxis and treatment of CNS leukemia and lymphoma ,107. Currently, other corticosteroids are not licensed for IT chemotherapy in children, but dexamethasone could be a potential candidate for IT administration. Non-human preclinical studies demonstrated both dexamethasone and prednisolone are quickly cleared from CSF after IT administration. Furthermore, after IV infusion dexamethasone shows higher free plasma levels compared to prednisolone, due to a lower plasma protein binding (70% for dexamethasone compared to 90% for prednisolone), resulting in greater penetration into the CSF compartment .The neurotoxicity of IT corticosteroids has not been proved yet by controlled studies, although several studies have described the potential development of psychiatric adverse effects, from depressive syndromes to psychosis, caused by the use of IT corticosteroids .Thiotepa is a lipid soluble alkylating agent available for IT administration, which doesn’t produce a significant advantage versus systemic thiotepa. In fact, after IV infusion both thiotepa and its active metabolite (TEPA) rapidly cross the BBB, so CSF and plasma exposure becomes almost equivalent. In addition, thiotepa and TEPA quickly diffuse out of the CSF, because of their conspicuous CSF clearance, which is nine times the rate of CSF flow ,111.IT thiotepa is proposed as a treatment of leptomeningeal metastases in children, but unfavorable outcomes observed in a lot of studies suggest that intrathecal thiotepa adds little to standard chemotherapy . Martín Some antineoplastic agents and monoclonal antibodies have been investigated for IT administration in preclinical and phase I/II studies; still, their clinical use remains limited.Oral 6-mercaptopurine (6-MP) is crucial for systemic maintenance therapy for ALL, but for the first time in 1991 Adamson et al. performed a phase I/II study of IT 6-mercaptopurine (6-MP) administration in pediatric patients with refractory meningeal leukemia. A 10-mg IT dose of 6-MP was administered twice weekly for 4 weeks, obtaining complete responses in four out of nine patients, with no significant toxicity . Topotecan is a topoisomerase I inhibitor with anti-tumor activity against many adult and childhood solid tumors. In preclinical and clinical studies, the CSF penetration of the active form of topotecan was approximately 30% after systemic administration, without neurologic toxicity ,116. PotBusulfan is an alkylating agent usually used as an oral formulation in high-dose chemotherapy schedules for allogenic and autologous bone marrow transplantation in patients with leukemia and solid tumors. A water-soluble microcrystalline formulation of Busulfan (Spartaject Busulfan) has been found to be active in non-human neoplastic meningitis and safe following IT injection in adult patients with leptomeningeal disease . In theiMafosfamide is a cyclophosphamide analogue which spontaneously degrades to 4-hydroxy-cyclophosphamide, a derivative which doesn’t require hepatic microsomal activation for antineoplastic activity. The effects of IT mafosfamide on various types of cancer cells were determined during preclinical investigations and clinical trials . In pediRituximab is an anti-CD20 antibody added to chemotherapy regimens mostly for systemic B-cell lymphoma, with improved efficacy and minimal side effects. Rituximab and the other monoclonal antibodies have low penetration in CSF with heterogeneity in the distribution within the tumor. Thus, their IT employ has been limited. In their study of patients with recurrent lymphomatous meningitis, Chamberlain et al. showed that the combination of IT liposomal Ara-C and rituximab produced modest palliative activity, without additional toxicity .In this section we show the most promising approaches currently in development for circumventing BBB and improving brain drug delivery, with the hope to forward potential future direction in this field, which may represent an example of personalized medicine.Intranasal drug delivery is emerging as a practical, safe and non-invasive method to bypass the BBB and deliver a wide range of neurotherapeutic agents to the brain. This approach has been primarily used to investigate therapeutic possibilities for neurological diseases  ,125. In regard to brain tumor therapy, INas delivery has been little investigated so far, but published results indicate interesting potential for this approach in treating CNS cancer. MTX, raltitrexed and 5-fluorouracil (5-FU) have all been shown to accumulate in the brain after INas delivery . After IFocused ultrasound is an emerging and non-invasive method to transiently increase permeability of the BBB . In 2001Several preclinical studies show efficacy of the delivery of anticancer drugs into the brain after FUS induced BBB disruption, such as with doxorubicin ,134, MTXConvection-enhanced delivery (CED) is a technique for circumventing BBB and delivering therapeutics directly through the interstitial spaces of the CNS, using intraparenchymal microcatheters with continuous positive-pressure infusion . Many clRecently however Souweidane et al. reported the first trial that demonstrates the efficacy and safety of CED of a radioimmunotherapy agent targeting the glioma-associated B7-H3 antigen in children with diffuse intrinsic pontine glioma (DIPG) who have previously received radiation therapy . CED couBBBD, IA and intracavitary chemotherapy seem to have a restricted application in the treatment of pediatric primary and metastatic CNS tumors, for their invasiveness and potentially serious adverse effects. Conversely, IT administration has been almost widely employed in the treatment of ALL and lymphoma to avoid the neurotoxicity of cranial irradiation. Intralumbar administration is safe and not painful if practiced with adequate deep sedation, whereas the implant of subcutaneous Ommaya reservoirs allows the release of a higher and more homogeneous drug concentration in the arachnoids space compared with intralumbar administration, avoiding stressful subsequent LP. However, Ommaya reservoirs are correlated with an elevated risk of infections or obstructive complications, which clinicians must try to prevent.IT administration in pediatric oncology is substantially limited to MTX, Ara-C and hydrocortisone, alone or in TIT, because of the extreme neurotoxicity of many other systemic antineoplastic agents, which if accidentally given by IT are described as usually fatal. Moreover, the IT dose of MTX, Ara-C and hydrocortisone are age-related and not BSA-related, to reduce the incidence of neurotoxic side effects, enhancing their effectiveness, especially in younger children. Finally, several antineoplastic agents and monoclonal antibodies have been investigated for IT administration in preclinical and phase I/II studies, but further studies are required to improve their clinical use.Future clinical studies should investigate the new promising approaches currently in development to improve brain drug delivery in pediatric primary and metastatic CNS tumors, such as INas or FUS-mediated drug delivery and CED.


Sepsis and bacterial meningitis are major causes of mortality and morbidity in neonates and infants. Meropenem, a broad-spectrum antibiotic, is not licensed for use in neonates and infants below 3 months of age and sufficient information on its plasma and CSF disposition and dosing in neonates and infants is lacking.To determine plasma and CSF pharmacokinetics of meropenem in neonates and young infants and the link between pharmacokinetics and clinical outcomes in babies with late-onset sepsis (LOS).n = 401) from 167 patients and opportunistic CSF samples (n = 78) from 56 patients were analysed.Data were collected in two recently conducted studies, i.e. NeoMero-1  and NeoMero-2 . Optimally timed plasma samples  were 16.7  L/h and 38.6  L, respectively. CSF penetration was low (8%), but rose with increasing CSF protein, with 40% penetration predicted at a protein concentration of 6 g/L. Increased infusion time improved plasma target attainment, but lowered CSF concentrations. For 24 patients with culture-proven Gram-negative LOS, pharmacodynamic target attainment was similar regardless of the test-of-cure visit outcome.Simulations showed that longer infusions increase plasma PTA but decrease CSF PTA. CSF penetration is worsened with long infusions so increasing dose frequency to achieve therapeutic targets should be considered. It is bactericidal against T>MIC). Approximately 40%T>MIC is believed to be sufficient for a bactericidal effect;,T>MIC.T > 5× MIC was suggested.,,Meropenem pharmacodynamics (PD) is usually described by the percentage of time when concentrations are above the MIC  in neonates and infants,In NeoMero-1, subjects were recruited from 15 centres in six different countries . There were, on average, 8 subjects/centre (range 1–25). In NeoMero-2, there were 21 centres in seven countries  recruiting, on average, 2 subjects/centre (range 1–9). Independent Ethics Committees in each country approved the studies, which were registered on EudraCT (2011-001515-31 and 2011-001521-25) and clinicaltrials.gov (NCT01551394 and NCT01554124).The inclusion criteria for NeoMero-1 were: diagnosis of sepsis and postnatal age (PNA) ≤90 days, and ≥72 h of life at sepsis onset. Sepsis was defined as: (i) sepsis confirmed by a positive bacterial culture, accompanied by an abnormal clinical or laboratory measurement; or (ii) clinical sepsis, i.e. in case the bacterial culture was negative, clinical and laboratory criteria defined by either EMAMeropenem was supplied by Chiesi Pharmaceuticals, Italy. A 12 hourly dose interval was used in patients with <32 weeks’ gestational age (GA) and <2 weeks’ PNA, and 8 hourly in all others. For LOS 20 mg/kg was used and for meningitis 40 mg/kg was used, these doses and frequencies having been previously used off-label. Meropenem was infused over 30 min, which was determined using a now-published modelPlasma sampling for PK was designed to minimize invasiveness whilst maximizing information gained. Three optimally timed PK samples were therefore decided to be taken from ≥56 subjects, spread evenly over each of the following age groups: <32 weeks GA and <2 weeks PNA; <32 weeks GA and ≥2 weeks PNA; ≥32 weeks GA and <2 weeks PNA; and ≥32 weeks GA and ≥2 weeks PNA. The remaining patients would contribute a single trough sample. Owing to logistical constraints in that meropenem could be started at any time, and in an emergency setting, PK sampling was planned to take place at steady-state. The PK model mentioned in the previous section and demographics from a previous European neonatal/infant sepsis studyBlood samples were immediately spun down and plasma extracted. All samples were frozen within 1 h of collection and stored at −80°C prior to analysis. Ultra-HPLC coupled with tandem MS (UHPLC-MS/MS) was used to determine plasma and CSF meropenem concentrations. To prepare 50 μL of plasma and CSF samples for the analysis, protein precipitation with methanol and microfiltration (using 0.22 μm filters), respectively, were used. In both cases we used ertapenem as an internal standard. The limit of detection for the plasma assay was 10 ng/mL and the limit of detection for the CSF assay was 2 ng/mL. The between-day variability was 4.1%–5% over the whole calibration range, including the limit of quantification (LoQ) of 80 ng/mL for the plasma assay; and 3%–5% for the CSF assay with an LoQ of 6 ng/mL.a priori in the model with allometric weight scaling and a function describing renal function maturation, respectively.P value of <0.01. The significance of a covariate was also tested by a randomization test,n = 1000) performed using PsN.PK modelling was undertaken using the first-order conditional estimation method with interaction (FOCEI) in NONMEM 7.3 .n = 1000) on the final model to test parameter robustness and derive uncertainty around the parameter estimates.Basic goodness-of-fit plots  in addition to visual predictive checks with 1000 replicates were used during model-building and to select the final model. A non-parametric bootstrap analysis was performed  or if treatment had to be modified at the discretion of the treating physician or the patient died (failure). These endpoints were measured at the test-of-cure visit 2 ± 1 days after the end of planned therapy (11 ± 3 days). The Kruskal–Wallis test was used to compare the two groups.For NeoMero-1 patients with culture-proven Gram-negative bloodstream infections for which an MIC could be determined and who received at least 24 h of treatment, the model was used to generate individual AUCn = 1000) using the final model estimates were used to generate %T>MIC curves for different dosing regimens and the following MIC values: 0.25, 0.5, 1, 2, 4, 8 and 16 mg/L. The unbound fraction of meropenem was fixed to 0.98.T>MIC curves were generated using plasma (for LOS) or CSF (for meningitis) predictions. The PD target was set to 61%T>MIC;Monte Carlo simulations  and 49 (including the 5 from NeoMero-1) in NeoMero-2. At enrolment, their median (range) weight was 2.12 (0.48–6.32) kg, PNA was 13 (1–90) days and gestational age (GA) was 33.3 (22.6–41.9) weeks. Patients from the NeoMero-1 study were more premature (median GA of 31.9 weeks versus 37.1 weeks in NeoMero-2) and therefore also weighed less than the patients from the NeoMero-2 study. Demographics are presented in Table Three optimally timed plasma samples were collected from 109 patients, whilst 44 provided a single trough sample. Sampling numbers from the remaining patients were two (nine patients), four (four patients) and seven (one patient). There was an even spread in PNA and GA of optimally timed samples with 25, 18, 20 and 24 patients, respectively, providing three optimal samples in each of the pre-defined age categories . Following sample analysis, 11 meropenem peak plasma samples were below 10 mg/L indicating possible data entry error and were thus excluded from the analysis to prevent biasing the model development. The influence of these data points was tested with the final plasma model and, although the changes in the typical final model parameter estimates were below 10%, the interindividual variability and the uncertainty approximately doubled, again giving a reason for their exclusion. The data set for model-building therefore contained 401 plasma samples and 78 CSF samples (CSF was obtained from 56 patients). The median (range) of CSF sampling time was 5.27 (0–12.0) h post-dose. Plots of the raw data are presented in Figure The final plasma population PK model was a one-compartment model. Weight was included with allometric scaling . However, since there were fewer missing measurements for CSF protein concentration , this covariate was included in the final model. The significance of the covariates was also confirmed by a randomization test.Initially, a proportional model was chosen for the residual error of both plasma and CSF data; however, Box–Cox power transformations of the residual errorThe diagnostic plots showed adequate fit to the data  Figure  and the P = 0.38). All patients had a %T>MIC above 61% (the proposed in vitro-derived target0–24:MIC ratio (P = 0.53) or in Cmin:MIC ratio (P = 0.73) in patients classified as treatment failures versus successes  and so a comparison of 20 and 40 mg/kg bolus versus continuous infusion are shown in Figure In the NeoMero-1 study there were 24 individuals with culture-proven LOS with a Gram-negative organism for which MIC values were available and at least 24 h of meropenem had been administered. Of these, 12 patients were considered to have been successfully treated (no need to modify the treatment course), whereas 12 patients failed . The mean MIC in the 12 successes was 0.27 mg/L, whereas in the 12 failures the mean MIC was 0.98 mg/L  to date involved six patients, who provided nine CSF samples.V = 1.17 L. These values are in agreement with what has been previously reported in the literature. For example, van den Anker et al.V was 0.97 L for a population of premature and mature infants. When only premature infants with an approximate weight of 1 kg were studied, the CL was lower, specifically 0.06 L/het al.;The values of the PK parameters for a typical infant from this study  were: CL = 0.39 L/h and ,Since meropenem showed low potential for nephrotoxicity,T>MIC, but decreases CSF %T>MIC.A PK model describing plasma and CSF meropenem data in young infants with confirmed or suspected LOS and/or meningitis was developed using data from one of the largest infant sepsis trials to have been conducted in this population. Dosing of 20 mg/kg as an 8 hourly bolus may be adequate for LOS at current MIC targets, but in future 40 mg/kg may be necessary owing to increasing pathogen MICs. Increasing infusion times (up to continuous infusion) improves circulating %


Background and aim: Granulocyte-colony-stimulating factor (G-CSF) is highly beneficial as a general treatment for anti-thyroid drug (ATD)-induced agranulocytosis. This meta-analysis aimed to assess the clinical effects of G-CSF and non-G-CSF on recovery duration in patients with ATD-induced agranulocytosis by analyzing the overall clinical outcomes.Methods: The PubMed, Embase, Ovid, Cochrane, Google Scholar, China National Knowledge Infrastructure (CNKI) databases were searched for published studies from 1900 to 2018. No language restriction was implemented.Results: This meta-analysis included 10 published retrospective studies and one prospective study. Data were obtained from 11 trials (474 patients: 247 with G-CSF and 227 with non-G-CSF treatment). Compared with the non-G-CSF group, the G-CSF group presented shorter recovery duration . However, the recovery duration varied across regions and recovery criteria. Asian patients achieved significant clinical outcomes [WMD = −3.16 days ] compared with European and South American patients [WMD = −2.19 days ]. Also, according to various recovery criteria, a duration of granulocyte count increase of more than 1.5 or 1.0 × 109/L [WMD = −3.50 days ] revealed a better treatment effect.Conclusion: G-CSF can significantly shorten the recovery duration in patients with ATD-induced agranulocytosis. It occurs in 12 out of 1,000 people, and 90% of patients are diagnosed with GD . Patient9/L . ATD-ind9/L . The typ9/L . ATD tre9/L .Nevertheless, the treatment of ATD-induced agranulocytosis has been controversial. Many retrospective studies have focused on ATD-induced agranulocytosis, and some concluded that G-CSF could shorten the recovery duration. However, some indicated that there was no statistically significant difference between the recovery duration in G-CSF and non-G-CSF groups. In 2006, the American Society of Clinical Oncology (ASCO) provided updated recommendations for patients with non-cancer- or non-chemotherapy-induced agranulocytosis. According to this guideline, patients with high-risk features, such as prolonged and profound agranulocytosis, an age of more than 65 years, uncontrolled primary diseases, pneumonia, hypertension, and multiorgan dysfunction, should take G-CSF as an adjunct treatment , 13. HowThis meta-analysis was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-analysis  and the A systematic literature review was performed based on the databases PubMed, Embase, Medline, Ovid, Cochrane, CNKI, and Google Scholar. The keywords used for the search were hyperthyroidism, GD, agranulocytosis, leukopenia, granulocyte colony-stimulating factor, and G-CSF. The search range was extended by combining the keywords with the following free words: low white blood cell count and granulocytopenia. The advanced search protocol was restricted to studies from 1900 to 2018, but the language was unrestricted. Two researchers, Yonghui Wang and Xiaoying Li, extracted the data independently.The inclusion criteria were as follows: (1) patients rigorously diagnosed with hyperthyroidism and agranulocytosis; (2) patients with ATD-induced agranulocytosis; (3) studies including both G-CSF and control groups; (4) recovery duration expressed as mean and standard deviation (SD), or median and range. The exclusion criteria were as follows: (1) studies without a control group; (2) patients from different case reports; (3) research enrolling few patients or fail to provide mean and SD, or median and range; (4) no statement that the granulocyte count was normal before hyperthyroidism; (5) abstracts, reviews, and case reports; and (6) agranulocytosis induced by other drugs.P-value was more than 0.1 assessed by funnel plots and Begg's test.The information was extracted from the included studies using a standard form by two reviewers (Yonghui Wang and Xiaoying Li) independently. Any disagreement was resolved by discussion with a third reviewer (Liang Zheng). The standard form included the following variables: author, publication year, journal, research period, patients' races, drug used in the studies, dosage of drugs, mean age of patients, number of female and male patients, dosage of G-CSF, number of patients, follow-up period, and mean and SD of G-CSF and non-G-CSF groups. The quality of the non-randomized studies was assessed using the Newcastle–Ottawa Scale with the following items: patient selection, comparability, and assessment of outcome. The studies scoring five or more stars were considered to be of high quality. Also, a checklist with 4–11 items, which was recommended by the Agency for Healthcare Research and Quality (AHRQ), was used as an alternative tool to assess the quality of studies. In this form, an item scored “0,” if the answer was “No” or “Not clear,” and “1,” if the answer was “Yes.” Scores of 8–11 indicated high quality; scores of 4–7 indicated moderate quality; and scores of 0–3 indicated low quality. Potential publication bias was assessed using funnel plots and was also evaluated using Begg's test. Publication bias was not existent when the 2 and I2 statistics. When the P-value was < 0.05 or the I2 value was <50%, the heterogeneity was acceptable and provided proof that the fixed-effects model could be used to calculate the pooled effect. A forest plot was used to summarize the effect of G-CSF compared with non-G-CSF in terms of shortened recovery duration and to estimate the 95% confidence interval. Meta-regression analysis was performed to identify the potential source of bias. Sensitivity analyses were also conducted, excluding one study of the pooled effects to balance one study's influence with overall effects.Stata software version 14  was used to perform the meta-analysis. For those only providing the median and range of recovery duration, the mean and SD were calculated as recommended by Stela Pudar Hozo . HeterogA flow chart for this meta-analysis is shown in 9/L, and nine studies clearly stated that agranulocytosis was diagnosed when the absolute granulocyte count was <0.5 × 109/L. Eight of 11 studies mentioned the recovery criteria. Among these, five studies reported the recovery of patients with a granulocyte count reaching 0.5 × 109/L, one study set the criteria to 1.0 × 109/L and 1.5 × 109/L, and four studies did not clarify the recovery criteria. All patients stopped taking ATDs when diagnosed with agranulocytosis. Only six studies reported the G-CSF doses of patients, which were 75, 300, 5, and 100–250 μg/day. The details of the included studies are shown in Of the 11 studies included, 10 were of a retrospective nature and one was a prospective study. The publication time ranged from 1993 to 2016. The participants came from different regions, including Asia, Europe, and South America. The medical records were retrieved from 1975 to 2016. Further, 247 patients received G-CSF after diagnosis with agranulocytosis and 227 received antibiotics, steroids, or no treatment. Five studies included patients with hyperthyroidism, revealing that the incidence of agranulocytosis in hyperthyroid patients ranged from 0.1 to 3.5%. One study did not clarify the definition of agranulocytosis, one study stated that the agranulocytosis was defined as an absolute granulocyte count of <2.0 × 10All studies were cohort studies except one, which was a prospective study. Hence, the quality prospective study was not assessed using the NOS scale or AHRQ form. Further, 36.4% (4/11) of the included studies were evaluated to be of high quality with more than five stars in the NOS scale, and 45.5% (5/11) were evaluated to be of moderate quality in the AHRQ form. The risk of bias of the included studies was low. As the control groups belonged to the same center, it was supposed that they came from the same community. The participants could moderately represent the G-CSF and non-G-CSF cohorts. Some studies failed to make a statement that agranulocytosis did not exist before hyperthyroidism, lacked comparability of the two groups in terms of age, sex ratio, and drug dose, or lacked follow-up to trace the later condition when considering the compatibility between G-CSF and non-G-CSF groups and outcomes of the studies. The diagnosis and characteristics of all patients were based on the hospital records, and therefore they were regarded as secure data. Most studies indicated that they excluded suspicious patients. Yet, most studies did not provide information on the exact follow-up period because they did not focus on the recurrence of agranulocytosis after the first treatment. Due to its low incidence, the value of studying recurrence was restricted. Hence, it was considered that the loss of follow-up would not produce bias.Eleven studies provided data on recovery duration depending on the application of G-CSF, except for one study, which had an elaborate design containing two subgroups: symptomatic and asymptomatic. As the differences in recovery duration in symptomatic and asymptomatic groups were statistically significant, they were segregated into two profiles.2 = 32.06 and I2 = 65.7%, P = 0.001) (Z = 4.43 P = 0.000)].The heterogeneity analysis showed that the data were heterogeneous (χ= 0.001) . The ranP = 0.000) (P = 0.409) proved that the mean recovery duration in the G-CSF group did not statistically significantly decrease compared with that in the non-G-CSF group  (9/L or 1.5 × 109/L (P = 0.000) presented larger differences between G-CSF and control groups compared with the criteria of a granulocyte count of more than 0.5 × 109/L.As nine Asian studies and two studies from Europe and South America were enrolled, it was considered that the pooled effects were more typical in Asia. The subtotal effects indicated that the pooled effects in Asia  a were stSF group b. Also, = 0.006) a. The re × 109/L b with WMZ value was 1.03 (P = 0.304)   which inNo previous meta-analysis has clarified the effect of G-CSF on agranulocytosis after hyperthyroidism. The guideline of the American Thyroid Association does not provide any recommendations to support or oppose the application of G-CSF in agranulocytosis after hyperthyroidism . The recThe present study demonstrated that G-CSF treatment was effective in ATD-induced agranulocytosis. The studies included were screened, and it was ascertained whether agranulocytosis occurred before ATD treatment. Therefore, it was believed that agranulocytosis after hyperthyroidism was associated with ATD. G-CSF could clearly shorten recovery duration compared with non-G-CSF treatment in which antibiotics and steroids were taken instead. In studies with patients in the non-G-CSF group taking antibiotics and steroids, the effect of G-CSF was also more significant. Hence, G-CSF was considered to be more effective than antibiotics or steroids. The exact differences among G-CSF, antibiotics, and steroids were not elaborated upon in the present study. Only six death cases were reported in three studies , 21, 24,9/L. Further, 91.1% (225) of Asian patients took G-CSF, and of 88.1% (200) Asian patients did not take G-CSF after agranulocytosis. The subtotal results indicated that Asian patients presented convincing treatment response with obvious statistical significance. On the contrary, the subgroup effect in Europe and South America was not in favor of a distinct effect in the G-CSF group. This meta-analysis did not conclude that G-CSF failed to shorten the recovery duration in Europe and South America because only two studies were included in this subgroup. Additionally, few pertinent studies were conducted in other regions, which unavoidably restricted the intention of this meta-analysis. Moreover, it was not concluded that the effect of G-CSF would be more significant in Asia or other regions, as GD varied with different diets and races. The main source of carnitine is the diet, especially red meat, poultry, fish, and dairy products  than in the symptomatic group  and the shorter recovery durations in the asymptomatic group (2.3 ± 1.0 days) than in the symptomatic group . In both groups, the recovery periods were dramatically shorter than those before the prescription of G-CSF. Importantly, this was the first meta-analysis to summarize the overall clinical effect of G-CSF on ATD-induced agranulocytosis.The strength of the present investigation was the strict process used in the search strategy so that the studies included were relatively homogeneous. To provide convincing data, Tajiri's study was separated into two profiles. Each subgroup was treated as an individual study because of the higher baseline of granulocyte count in the asymptomatic group (0.320 × 10Nevertheless, this study had inevitable limitations. First, the baseline of the patients was varied. At the same time, some studies missed detailed information, such as the granulocyte count at the onset of agranulocytosis, follow-up period, or G-CSF dose. If the granulocyte count at onset was much lower, it was supposed that the recovery duration would be longer. Second, the association of ATD dosage with the recovery period could not be analyzed. Few studies provided the mean and SD of ATD dosage. Hence, it was not possible to make a subgroup analysis according to the ATD dosage. Third, the G-CSF dosage would be another prominent factor vital to guiding the medical decision. Some studies involved the use of varied dosages for patients, while some did not even mention the dosage. Thus, the meta-analysis could not offer further advice on G-CSF dosage. Fourth, nine studies were from Asia and two were from other regions, which made the conclusion more dependable when applied to Asian patients. The research strategy was expanded to more databases, but only two studies from South America and Europe were eventually found to be eligible for inclusion. Last but not least, 9 out of 10 included studies were of retrospective nature, which might weaken the strength of this meta-analysis. Due to the low frequency of ATD-induced agranulocytosis, prospective studies take a long time. Hence, only one prospective study was found. All these limitations were objective and difficult to overcome. Further studies should be conducted to provide detailed information on the characteristics at onset and G-CSF dosage so as to optimize the findings of the present analysis.G-CSF could effectively shorten the recovery duration in patients with ATD-induced agranulocytosis compared with non-GCSF treatment.All datasets generated for this study are included in the article/supplementary material.YW and XL were involved in drafting the manuscript and made a contribution to conception and design. QY engaged in the analysis and interpretation of the data. WW and YZ were involved in draft revision. JL contributed to the acquisition of data. BZ and LZ gave final approval to the version to be published. All authors agreed to be accountable for all aspects of the study in ensuring that questions related to the accuracy and integrity of any part of the study were appropriately investigated and resolved.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.