Environmental, sex-specific and genetic determinants of infant social behaviour in a wild primate.

Affiliative social bonds are linked to fitness components in many social mammals. However, despite their importance, little is known about how the tendency to form social bonds develops in young animals, or if the timing of development is heritable and thus can evolve. Using four decades of longitudinal observational data from a wild baboon population, we assessed the environmental determinants of an important social developmental milestone in baboons-the age at which a young animal first grooms a conspecific-and we assessed how the rates at which offspring groom their mothers develops during the juvenile period. We found that grooming development differs between the sexes: female infants groom at an earlier age and reach equal rates of grooming with their mother earlier than males. We also found that age at first grooming for both sexes is weakly heritable (h2 = 0.043, 95% CI: 0.002-0.110). These results show that sex differences in grooming emerge at a young age; that strong, equitable social relationships between mothers and daughters begin very early in life; and that age at first grooming is heritable and therefore can be shaped by natural selection.

Using the LeiCNS-PK3.0 Physiologically-Based Pharmacokinetic Model to Predict Brain Extracellular Fluid Pharmacokinetics in Mice.

INTRODUCTION: The unbound brain extracelullar fluid (brainECF) to plasma steady state partition coefficient, Kp,uu,BBB, values provide steady-state information on the extent of blood-brain barrier (BBB) transport equilibration, but not on pharmacokinetic (PK) profiles seen by the brain targets. Mouse models are frequently used to study brain PK, but this information cannot directly be used to inform on human brain PK, given the different CNS physiology of mouse and human. Physiologically based PK (PBPK) models are useful to translate PK information across species. AIM: Use the LeiCNS-PK3.0 PBPK model, to predict brain extracellular fluid PK in mice. METHODS: Information on mouse brain physiology was collected from literature. All available connected data on unbound plasma, brainECF PK of 10 drugs (cyclophosphamide, quinidine, erlotonib, phenobarbital, colchicine, ribociclib, topotecan, cefradroxil, prexasertib, and methotrexate) from different mouse strains were used. Dosing regimen dependent plasma PK was modelled, and Kpuu,BBB values were estimated, and provided as input into the LeiCNS-PK3.0 model to result in prediction of PK profiles in brainECF. RESULTS: Overall, the model gave an adequate prediction of the brainECF PK profile for 7 out of the 10 drugs. For 7 drugs, the predicted versus observed brainECF data was within two-fold error limit and the other 2 drugs were within five-fold error limit. CONCLUSION: The current version of the mouse LeiCNS-PK3.0 model seems to reasonably predict available information on brainECF from healthy mice for most drugs. This brings the translation between mouse and human brain PK one step further.

Status of Metabolomic Measurement for Insights in Alzheimer's Disease Progression-What Is Missing?

Alzheimer's disease (AD) is an aging-related neurodegenerative disease, leading to the progressive loss of memory and other cognitive functions. As there is still no cure for AD, the growth in the number of susceptible individuals represents a major emerging threat to public health. Currently, the pathogenesis and etiology of AD remain poorly understood, while no efficient treatments are available to slow down the degenerative effects of AD. Metabolomics allows the study of biochemical alterations in pathological processes which may be involved in AD progression and to discover new therapeutic targets. In this review, we summarized and analyzed the results from studies on metabolomics analysis performed in biological samples of AD subjects and AD animal models. Then this information was analyzed by using MetaboAnalyst to find the disturbed pathways among different sample types in human and animal models at different disease stages. We discuss the underlying biochemical mechanisms involved, and the extent to which they could impact the specific hallmarks of AD. Then we identify gaps and challenges and provide recommendations for future metabolomics approaches to better understand AD pathogenesis.

Altered protein expression of membrane transporters in isolated cerebral microvessels and brain cortex of a rat Alzheimer's disease model.

There is growing evidence that membrane transporters expressed at the blood-brain barrier (BBB) and brain parenchymal cells play an important role in Alzheimer's disease (AD) development and progression. However, quantitative information about changes in transporter protein expression at neurovascular unit cells in AD is limited. Here, we studied the changes in the absolute protein expression of five ATP-binding cassette (ABC) and thirteen solute carrier (SLC) transporters in the isolated brain microvessels and brain cortical tissue of TgF344-AD rats compared to age-matched wild-type (WT) animals using liquid chromatography tandem mass spectrometry based quantitative targeted absolute proteomic analysis. Moreover, sex-specific alterations in transporter expression in the brain cortical tissue of this model were examined. Protein expressions of Abcg2, Abcc1 and FATP1 (encoded by Slc27a1) in the isolated brain microvessels of TgF344-AD rats were 3.1-, 2.0-, 4.3-fold higher compared to WT controls, respectively (p < 0.05). Abcc1 and 4F2hc (encoded by Slc3a2) protein expression was significantly up-regulated in the brain cortical tissue of male TgF344-AD rats compared to male WT rats (p < 0.05). The study provides novel information for the elucidation of molecular mechanisms underlying AD and valuable knowledge about the optimal use of the TgF344-AD rat model in AD drug development and drug delivery research.

The Extension of the LeiCNS-PK3.0 Model in Combination with the "Handshake" Approach to Understand Brain Tumor Pathophysiology.

Micrometastatic brain tumor cells, which cause recurrence of malignant brain tumors, are often protected by the intact blood-brain barrier (BBB). Therefore, it is essential to deliver effective drugs across not only the disrupted blood-tumor barrier (BTB) but also the intact BBB to effectively treat malignant brain tumors. Our aim is to predict pharmacokinetic (PK) profiles in brain tumor regions with the disrupted BTB and the intact BBB to support the successful drug development for malignant brain tumors. LeiCNS-PK3.0, a comprehensive central nervous system (CNS) physiologically based pharmacokinetic (PBPK) model, was extended to incorporate brain tumor compartments. Most pathophysiological parameters of brain tumors were obtained from literature and two missing parameters of the BTB, paracellular pore size and expression level of active transporters, were estimated by fitting existing data, like a "handshake". Simultaneous predictions were made for PK profiles in extracellular fluids (ECF) of brain tumors and normal-appearing brain and validated on existing data for six small molecule anticancer drugs. The LeiCNS-tumor model predicted ECF PK profiles in brain tumor as well as normal-appearing brain in rat brain tumor models and high-grade glioma patients within twofold error for most data points, in combination with estimated paracellular pore size of the BTB and active efflux clearance at the BTB. Our model demonstrated a potential to predict PK profiles of small molecule drugs in brain tumors, for which quantitative information on pathophysiological alterations is available, and contribute to the efficient and successful drug development for malignant brain tumors.

Drug Distribution in Brain and Cerebrospinal Fluids in Relation to IC50 Values in Aging and Alzheimer's Disease, Using the Physiologically Based LeiCNS-PK3.0 Model.

BACKGROUND: Very little knowledge exists on the impact of Alzheimer's disease on the CNS target site pharmacokinetics (PK). AIM: To predict the CNS PK of cognitively healthy young and elderly and of Alzheimer's patients using the physiologically based LeiCNS-PK3.0 model. METHODS: LeiCNS-PK3.0 was used to predict the PK profiles in brain extracellular (brainECF) and intracellular (brainICF) fluids and cerebrospinal fluid of the subarachnoid space (CSFSAS) of donepezil, galantamine, memantine, rivastigmine, and semagacestat in young, elderly, and Alzheimer's patients. The physiological parameters of LeiCNS-PK3.0 were adapted for aging and Alzheimer's based on an extensive literature search. The CNS PK profiles at plateau for clinical dose regimens were related to in vitro IC50 values of acetylcholinesterase, butyrylcholinesterase, N-methyl-D-aspartate, or gamma-secretase. RESULTS: The PK profiles of all drugs differed between the CNS compartments regarding plateau levels and fluctuation. BrainECF, brainICF and CSFSAS PK profile relationships were different between the drugs. Aging and Alzheimer's had little to no impact on CNS PK. Rivastigmine acetylcholinesterase IC50 values were not reached. Semagacestat brain PK plateau levels were below the IC50 of gamma-secretase for half of the interdose interval, unlike CSFSAS PK profiles that were consistently above IC50. CONCLUSION: This study provides insights into the relations between CNS compartments PK profiles, including target sites. CSFSAS PK appears to be an unreliable predictor of brain PK. Also, despite extensive changes in blood-brain barrier and brain properties in Alzheimer's, this study shows that the impact of aging and Alzheimer's pathology on CNS distribution of the five drugs is insignificant.

Can You Trust Who You See? The Evolution of Socially Cued Anticipatory Plasticity.

AbstractThe social environment can affect development and fitness. However, we do not know how selection acts on individuals that cue developmental pathways using features of the social environment. Socially cued anticipatory plasticity (SCAP) is a hypothetical strategy whereby juveniles use social cues to alter development to match their adult phenotype to the social environment that they expect to encounter. While intuitively appealing, the evolution of such plasticity is a puzzle, because the cue changes when individuals use it. Can socially cued plasticity evolve when such a feedback occurs? We use individual-based simulations to model evolution of SCAP in an environment that fluctuates between favoring each of two discrete phenotypes. We found that socially cued plasticity evolved, but only when strong selection acted on survival rather than on fecundity differences between adult phenotypes. In this case, the social cue reliably predicted which phenotype would be favored on maturation. Surprisingly, costs to plasticity increased the range of conditions under which it was adaptive. In the absence of costs, evolution led to a state where SCAP individuals could not effectively respond to environmental changes. Costs to plasticity lowered the proportion of the population that used SCAP, which in turn increased the reliability of the social cue and allowed individuals that used socially cued plasticity to switch between the favored phenotypes more consistently. Our results suggest that the evolution of adaptive plasticity in response to social cues may represent a larger class of problems in which evolution is hard to predict because of feedbacks among critical processes.

Lumbar cerebrospinal fluid-to-brain extracellular fluid surrogacy is context-specific: insights from LeiCNS-PK3.0 simulations.

Predicting brain pharmacokinetics is critical for central nervous system (CNS) drug development yet difficult due to ethical restrictions of human brain sampling. CNS pharmacokinetic (PK) profiles are often altered in CNS diseases due to disease-specific pathophysiology. We previously published a comprehensive CNS physiologically-based PK (PBPK) model that predicted the PK profiles of small drugs at brain and cerebrospinal fluid compartments. Here, we improved this model with brain non-specific binding and pH effect on drug ionization and passive transport. We refer to this improved model as Leiden CNS PBPK predictor V3.0 (LeiCNS-PK3.0). LeiCNS-PK3.0 predicted the unbound drug concentrations of brain ECF and CSF compartments in rats and humans with less than two-fold error. We then applied LeiCNS-PK3.0 to study the effect of altered cerebrospinal fluid (CSF) dynamics, CSF volume and flow, on brain extracellular fluid (ECF) pharmacokinetics. The effect of altered CSF dynamics was simulated using LeiCNS-PK3.0 for six drugs and the resulting drug exposure at brain ECF and lumbar CSF were compared. Simulation results showed that altered CSF dynamics changed the CSF PK profiles, but not the brain ECF profiles, irrespective of the drug's physicochemical properties. Our analysis supports the notion that lumbar CSF drug concentration is not an accurate surrogate of brain ECF, particularly in CNS diseases. Systems approaches account for multiple levels of CNS complexity and are better suited to predict brain PK.

Imaging Techniques in Alzheimer's Disease: A Review of Applications in Early Diagnosis and Longitudinal Monitoring.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting many individuals worldwide with no effective treatment to date. AD is characterized by the formation of senile plaques and neurofibrillary tangles, followed by neurodegeneration, which leads to cognitive decline and eventually death. INTRODUCTION: In AD, pathological changes occur many years before disease onset. Since disease-modifying therapies may be the most beneficial in the early stages of AD, biomarkers for the early diagnosis and longitudinal monitoring of disease progression are essential. Multiple imaging techniques with associated biomarkers are used to identify and monitor AD. AIM: In this review, we discuss the contemporary early diagnosis and longitudinal monitoring of AD with imaging techniques regarding their diagnostic utility, benefits and limitations. Additionally, novel techniques, applications and biomarkers for AD research are assessed. FINDINGS: Reduced hippocampal volume is a biomarker for neurodegeneration, but atrophy is not an AD-specific measure. Hypometabolism in temporoparietal regions is seen as a biomarker for AD. However, glucose uptake reflects astrocyte function rather than neuronal function. Amyloid-ß (Aß) is the earliest hallmark of AD and can be measured with positron emission tomography (PET), but Aß accumulation stagnates as disease progresses. Therefore, Aß may not be a suitable biomarker for monitoring disease progression. The measurement of tau accumulation with PET radiotracers exhibited promising results in both early diagnosis and longitudinal monitoring, but large-scale validation of these radiotracers is required. The implementation of new processing techniques, applications of other imaging techniques and novel biomarkers can contribute to understanding AD and finding a cure. CONCLUSIONS: Several biomarkers are proposed for the early diagnosis and longitudinal monitoring of AD with imaging techniques, but all these biomarkers have their limitations regarding specificity, reliability and sensitivity. Future perspectives. Future research should focus on expanding the employment of imaging techniques and identifying novel biomarkers that reflect AD pathology in the earliest stages.

The 3D Brain Unit Network Model to Study Spatial Brain Drug Exposure under Healthy and Pathological Conditions.

PURPOSE: We have developed a 3D brain unit network model to understand the spatial-temporal distribution of a drug within the brain under different (normal and disease) conditions. Our main aim is to study the impact of disease-induced changes in drug transport processes on spatial drug distribution within the brain extracellular fluid (ECF). METHODS: The 3D brain unit network consists of multiple connected single 3D brain units in which the brain capillaries surround the brain ECF. The model includes the distribution of unbound drug within blood plasma, coupled with the distribution of drug within brain ECF and incorporates brain capillaryblood flow, passive paracellular and transcellular BBB transport, active BBB transport, brain ECF diffusion, brain ECF bulk flow, and specific and nonspecific brain tissue binding. All of these processes may change under disease conditions. RESULTS: We show that the simulated disease-induced changes in brain tissue characteristics significantly affect drug concentrations within the brain ECF. CONCLUSIONS: We demonstrate that the 3D brain unit network model is an excellent tool to gain understanding in the interdependencies of the factors governing spatial-temporal drug concentrations within the brain ECF. Additionally, the model helps in predicting the spatial-temporal brain ECF concentrations of existing drugs, under both normal and disease conditions.

Utility of Animal Models to Understand Human Alzheimer's Disease, Using the Mastermind Research Approach to Avoid Unnecessary Further Sacrifices of Animals.

To diagnose and treat early-stage (preclinical) Alzheimer's disease (AD) patients, we need body-fluid-based biomarkers that reflect the processes that occur in this stage, but current knowledge on associated processes is lacking. As human studies on (possible) onset and early-stage AD would be extremely expensive and time-consuming, we investigate the potential value of animal AD models to help to fill this knowledge gap. We provide a comprehensive overview of processes associated with AD pathogenesis and biomarkers, current knowledge on AD-related biomarkers derived from on human and animal brains and body fluids, comparisons of biomarkers obtained in human AD and frequently used animal AD models, and emerging body-fluid-based biomarkers. In human studies, amyloid beta (Aß), hyperphosphorylated tau (P-tau), total tau (T-tau), neurogranin, SNAP-25, glial fibrillary acidic protein (GFAP), YKL-40, and especially neurofilament light (NfL) are frequently measured. In animal studies, the emphasis has been mostly on Aß. Although a direct comparison between human (familial and sporadic) AD and (mostly genetic) animal AD models cannot be made, still, in brain, cerebrospinal fluid (CSF), and blood, a majority of similar trends are observed for human AD stage and animal AD model life stage. This indicates the potential value of animal AD models in understanding of the onset and early stage of AD. Moreover, animal studies can be smartly designed to provide mechanistic information on the interrelationships between the different AD processes in a longitudinal fashion and may also include the combinations of different conditions that may reflect comorbidities in human AD, according to the Mastermind Research approach.

Mechanistic Study on the Use of the l-Type Amino Acid Transporter 1 for Brain Intracellular Delivery of Ketoprofen via Prodrug: A Novel Approach Supporting the Development of Prodrugs for Intracellular Targets.

l-Type amino acid transporter 1 (LAT1), selectively expressed at the blood-brain barrier (BBB) and brain parenchymal cells, mediates brain delivery of drugs and prodrugs such as l-dopa and gabapentin. Although knowledge about BBB transport of LAT1-utilizing prodrugs is available, there is a lack of quantitative information about brain intracellular delivery and influence of prodrugs on the transporter's physiological state. We studied the LAT1-mediated intrabrain distribution of a recently developed prodrug of the cyclooxygenase inhibitor ketoprofen as well as its impact on transporter protein expression and function (i.e., amino acid exchange) using brain slice method in mice and rats. The intrabrain distribution of the prodrug was 16 times higher than that of ketoprofen. LAT1 involvement in brain cellular barrier uptake of the prodrug was confirmed, reflected by a higher unbound brain intracellular compared to brain extracellular fluid concentration. The prodrug did not alter LAT1 protein expression and amino acid exchange. Integration of derived parameters with previously performed in vivo pharmacokinetic study using the Combinatory Mapping Approach allowed to estimate the brain extra- and intracellular levels of unbound ketoprofen, prodrug, and released parent drug. The overall efficiency of plasma to brain intracellular delivery of prodrug-released ketoprofen was 11 times higher than after ketoprofen dosing. In summary, this study provides quantitative information supporting the use of the LAT1-mediated prodrug approach for enhanced brain delivery of drugs with intracellular targets.

In Vivo Quantitative Understanding of PEGylated Liposome's Influence on Brain Delivery of Diphenhydramine.

Despite the promising features of liposomes as brain drug delivery vehicles, it remains uncertain how they influence the brain uptake in vivo. In order to gain a better fundamental understanding of the interaction between liposomes and the blood-brain barrier (BBB), it is indispensable to test if liposomes affect drugs with different BBB transport properties (active influx or efflux) differently. The aim of this study was to quantitatively evaluate how PEGylated (PEG) liposomes influence brain delivery of diphenhydramine (DPH), a drug with active influx at the BBB, in rats. The brain uptake of DPH after 30 min intravenous infusion of free DPH, PEG liposomal DPH, or free DPH + empty PEG liposomes was compared by determining the unbound DPH concentrations in brain interstitial fluid and plasma with microdialysis. Regular blood samples were taken to measure total DPH concentrations in plasma. Free DPH was actively taken up into the brain time-dependently, with higher uptake at early time points followed by an unbound brain-to-plasma exposure ratio ( Kp,uu) of 3.0. The encapsulation in PEG liposomes significantly decreased brain uptake of DPH, with a reduction of Kp,uu to 1.5 ( p < 0.05). When empty PEG liposomes were coadministered with free drug, DPH brain uptake had a tendency to decrease ( Kp,uu 2.3), and DPH was found to bind to the liposomes. This study showed that PEG liposomes decreased the brain delivery of DPH in a complex manner, contributing to the understanding of the intricate interactions between drug, liposomes, and the BBB.

Correction to: Modelling the delay between pharmacokinetics and EEG effects of morphine in rats: binding kinetic versus effect compartment models.

The original version of this article was published open access. Unfortunately, due to a technical issue, the copyright holder name in the online version (HTML and XML) is incorrectly published as "Springer Science+Business Media, LLC, part of Springer Nature 2018". Instead, it should be "The Author(s) 2018".

Modelling the delay between pharmacokinetics and EEG effects of morphine in rats: binding kinetic versus effect compartment models.

Drug-target binding kinetics (as determined by association and dissociation rate constants, kon and koff) can be an important determinant of the kinetics of drug action. However, the effect compartment model is used most frequently instead of a target binding model to describe hysteresis. Here we investigate when the drug-target binding model should be used in lieu of the effect compartment model. The utility of the effect compartment (EC), the target binding kinetics (TB) and the combined effect compartment-target binding kinetics (EC-TB) model were tested on either plasma (ECPL, TBPL and EC-TBPL) or brain extracellular fluid (ECF) (ECECF, TBECF and EC-TBECF) morphine concentrations and EEG amplitude in rats. It was also analyzed when a significant shift in the time to maximal target occupancy (TmaxTO) with increasing dose, the discriminating feature between the TB and EC model, occurs in the TB model. All TB models assumed a linear relationship between target occupancy and drug effect on the EEG amplitude. All three model types performed similarly in describing the morphine pharmacodynamics data, although the EC model provided the best statistical result. The analysis of the shift in TmaxTO (∆TmaxTO) as a result of increasing dose revealed that ∆TmaxTO is decreasing towards zero if the koff is much smaller than the elimination rate constant or if the target concentration is larger than the initial morphine concentration. The results for the morphine PKPD modelling and the analysis of ∆TmaxTO indicate that the EC and TB models do not necessarily lead to different drug effect versus time curves for different doses if a delay between drug concentrations and drug effect (hysteresis) is described. Drawing mechanistic conclusions from successfully fitting one of these two models should therefore be avoided. Since the TB model can be informed by in vitro measurements of kon and koff, a target binding model should be considered more often for mechanistic modelling purposes.

Target-Site Investigation for the Plasma Prolactin Response: Mechanism-Based Pharmacokinetic-Pharmacodynamic Analysis of Risperidone and Paliperidone in the Rat.

To understand the drivers in the biological system response to dopamine D2 receptor antagonists, a mechanistic semiphysiologically based (PB) pharmacokinetic-pharmacodymanic (PKPD) model was developed to describe prolactin responses to risperidone (RIS) and its active metabolite paliperidone (PAL). We performed a microdialysis study in rats to obtain detailed plasma, brain extracellular fluid (ECF), and cerebrospinal fluid (CSF) concentrations of PAL and RIS. To assess the impact of P-glycoprotein (P-gp) functioning on brain distribution, we performed experiments in the absence or presence of the P-gp inhibitor tariquidar (TQD). PK and PKPD modeling was performed by nonlinear mixed-effect modeling. Plasma, brain ECF, and CSF PK values of RIS and PAL were well described by a 12-compartmental semi-PBPK model, including metabolic conversion of RIS to PAL. P-gp efflux functionality was identified on brain ECF for RIS and PAL and on CSF only for PAL. In the PKPD analysis, the plasma drug concentrations were more relevant than brain ECF or CSF concentrations to explain the prolactin response; the estimated EC50 was in accordance with reports in the literature for both RIS and PAL. We conclude that for RIS and PAL, the plasma concentrations better explain the prolactin response than do brain ECF or CSF concentrations. This research shows that PKPD modeling is of high value to delineate the target site of drugs.

A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations.

PURPOSE: Predicting target site drug concentration in the brain is of key importance for the successful development of drugs acting on the central nervous system. We propose a generic mathematical model to describe the pharmacokinetics in brain compartments, and apply this model to predict human brain disposition. METHODS: A mathematical model consisting of several physiological brain compartments in the rat was developed using rich concentration-time profiles from nine structurally diverse drugs in plasma, brain extracellular fluid, and two cerebrospinal fluid compartments. The effect of active drug transporters was also accounted for. Subsequently, the model was translated to predict human concentration-time profiles for acetaminophen and morphine, by scaling or replacing system- and drug-specific parameters in the model. RESULTS: A common model structure was identified that adequately described the rat pharmacokinetic profiles for each of the nine drugs across brain compartments, with good precision of structural model parameters (relative standard error <37.5%). The model predicted the human concentration-time profiles in different brain compartments well (symmetric mean absolute percentage error <90%). CONCLUSIONS: A multi-compartmental brain pharmacokinetic model was developed and its structure could adequately describe data across nine different drugs. The model could be successfully translated to predict human brain concentrations.

CNS Viral Infections-What to Consider for Improving Drug Treatment: A Plea for Using Mathematical Modeling Approaches.

Neurotropic viruses may cause meningitis, myelitis, encephalitis, or meningoencephalitis. These inflammatory conditions of the central nervous system (CNS) may have serious and devastating consequences if not treated adequately. In this review, we first summarize how neurotropic viruses can enter the CNS by (1) crossing the blood-brain barrier or blood-cerebrospinal fluid barrier; (2) invading the nose via the olfactory route; or (3) invading the peripheral nervous system. Neurotropic viruses may then enter the intracellular space of brain cells via endocytosis and/or membrane fusion. Antiviral drugs are currently used for different viral CNS infections, even though their use and dosing regimens within the CNS, with the exception of acyclovir, are minimally supported by clinical evidence. We therefore provide considerations to optimize drug treatment(s) for these neurotropic viruses. Antiviral drugs should cross the blood-brain barrier/blood cerebrospinal fluid barrier and pass the brain cellular membrane to inhibit these viruses inside the brain cells. Some antiviral drugs may also require intracellular conversion into their active metabolite(s). This illustrates the need to better understand these mechanisms because these processes dictate drug exposure within the CNS that ultimately determine the success of antiviral drugs for CNS infections. Finally, we discuss mathematical model-based approaches for optimizing antiviral treatments. Thereby emphasizing the potential of CNS physiologically based pharmacokinetic models because direct measurement of brain intracellular exposure in living humans faces ethical restrictions. Existing physiologically based pharmacokinetic models combined with in vitro pharmacokinetic/pharmacodynamic information can be used to predict drug exposure and evaluate efficacy of antiviral drugs within the CNS, to ultimately optimize the treatments of CNS viral infections.