A neural network model for the evolution of learning in changing environments.

Learning from past experience is an important adaptation and theoretical models may help to understand its evolution. Many of the existing models study simple phenotypes and do not consider the mechanisms underlying learning while the more complex neural network models often make biologically unrealistic assumptions and rarely consider evolutionary questions. Here, we present a novel way of modelling learning using small neural networks and a simple, biology-inspired learning algorithm. Learning affects only part of the network, and it is governed by the difference between expectations and reality. We use this model to study the evolution of learning under various environmental conditions and different scenarios for the trade-off between exploration (learning) and exploitation (foraging). Efficient learning readily evolves in our individual-based simulations. However, in line with previous studies, the evolution of learning is less likely in relatively constant environments, where genetic adaptation alone can lead to efficient foraging, or in short-lived organisms that cannot afford to spend much of their lifetime on exploration. Once learning does evolve, the characteristics of the learning strategy (i.e. the duration of the learning period and the learning rate) and the average performance after learning are surprisingly little affected by the frequency and/or magnitude of environmental change. In contrast, an organism's lifespan and the distribution of resources in the environment have a clear effect on the evolved learning strategy: a shorter lifespan or a broader resource distribution lead to fewer learning episodes and larger learning rates. Interestingly, a longer learning period does not always lead to better performance, indicating that the evolved neural networks differ in the effectiveness of learning. Overall, however, we show that a biologically inspired, yet relatively simple, learning mechanism can evolve to lead to an efficient adaptation in a changing environment.

The sex-specific factor SOA controls dosage compensation in Anopheles mosquitoes.

The Anopheles mosquito is one of thousands of species in which sex differences play a central part in their biology, as only females need a blood meal to produce eggs. Sex differentiation is regulated by sex chromosomes, but their presence creates a dosage imbalance between males (XY) and females (XX). Dosage compensation (DC) can re-equilibrate the expression of sex chromosomal genes. However, because DC mechanisms have only been fully characterized in a few model organisms, key questions about its evolutionary diversity and functional necessity remain unresolved1. Here we report the discovery of a previously uncharacterized gene (sex chromosome activation (SOA)) as a master regulator of DC in the malaria mosquito Anopheles gambiae. Sex-specific alternative splicing prevents functional SOA protein expression in females. The male isoform encodes a DNA-binding protein that binds the promoters of active X chromosomal genes. Expressing male SOA is sufficient to induce DC in female cells. Male mosquitoes lacking SOA or female mosquitoes ectopically expressing the male isoform exhibit X chromosome misregulation, which is compatible with viability but causes developmental delay. Thus, our molecular analyses of a DC master regulator in a non-model organism elucidates the evolutionary steps that lead to the establishment of a chromosome-specific fine-tuning mechanism.

Translational Modeling in Schizophrenia: Predicting Human Dopamine D2 Receptor Occupancy.

OBJECTIVES: To assess the ability of a previously developed hybrid physiology-based pharmacokinetic-pharmacodynamic (PBPKPD) model in rats to predict the dopamine D2 receptor occupancy (D2RO) in human striatum following administration of antipsychotic drugs. METHODS: A hybrid PBPKPD model, previously developed using information on plasma concentrations, brain exposure and D2RO in rats, was used as the basis for the prediction of D2RO in human. The rat pharmacokinetic and brain physiology parameters were substituted with human population pharmacokinetic parameters and human physiological information. To predict the passive transport across the human blood-brain barrier, apparent permeability values were scaled based on rat and human brain endothelial surface area. Active efflux clearance in brain was scaled from rat to human using both human brain endothelial surface area and MDR1 expression. Binding constants at the D2 receptor were scaled based on the differences between in vitro and in vivo systems of the same species. The predictive power of this physiology-based approach was determined by comparing the D2RO predictions with the observed human D2RO of six antipsychotics at clinically relevant doses. RESULTS: Predicted human D2RO was in good agreement with clinically observed D2RO for five antipsychotics. Models using in vitro information predicted human D2RO well for most of the compounds evaluated in this analysis. However, human D2RO was under-predicted for haloperidol. CONCLUSIONS: The rat hybrid PBPKPD model structure, integrated with in vitro information and human pharmacokinetic and physiological information, constitutes a scientific basis to predict the time course of D2RO in man.

Dopamine D2 receptor occupancy as a predictor of catalepsy in rats: a pharmacokinetic-pharmacodynamic modeling approach.

OBJECTIVES: Dopamine D2 receptor occupancy (D2RO) is the major determinant of efficacy and safety in schizophrenia drug therapy. Excessive D2RO (>80%) is known to cause catalepsy (CAT) in rats and extrapyramidal side effects (EPS) in human. The objective of this study was to use pharmacokinetic and pharmacodynamic modeling tools to relate CAT with D2RO in rats and to compare that with the relationship between D2RO and EPS in humans. METHODS: Severity of CAT was assessed in rats at hourly intervals over a period of 8 h after antipsychotic drug treatment. An indirect response model with and without Markov elements was used to explain the relationship of D2RO and CAT. RESULTS: Both models explained the CAT data well for olanzapine, paliperidone and risperidone. However, only the model with the Markov elements predicted the CAT severity well for clozapine and haloperidol. The relationship between CAT scores in rat and EPS scores in humans was implemented in a quantitative manner. Risk of EPS not exceeding 10% over placebo correlates with less than 86% D2RO and less than 30% probability of CAT events in rats. CONCLUSION: A quantitative relationship between rat CAT and human EPS was elucidated and may be used in drug discovery to predict the risk of EPS in humans from D2RO and CAT scores measured in rats.

Population pharmacokinetic-pharmacodynamic modeling of haloperidol in patients with schizophrenia using positive and negative syndrome rating scale.

The aim of this study was to develop a pharmacokinetic-pharmacodynamic (PKPD) model that quantifies the efficacy of haloperidol, accounting for the placebo effect, the variability in exposure-response, and the dropouts. Subsequently, the developed model was utilized to characterize an effective dosing strategy for using haloperidol as a comparator drug in future antipsychotic drug trials. The time course of plasma haloperidol concentrations from 122 subjects and the Positive and Negative Syndrome Scale (PANSS) scores from 473 subjects were used in this analysis. A nonlinear mixed-effects modeling approach was utilized to describe the time course of PK and PANSS scores. Bootstrapping and simulation-based methods were used for the model evaluation. A 2-compartment model adequately described the haloperidol PK profiles. The Weibull and Emax models were able to describe the time course of the placebo and the drug effects, respectively. An exponential model was used to account for dropouts. Joint modeling of the PKPD model with dropout model indicated that the probability of patients dropping out is associated with the observed high PANSS score. The model evaluation results confirmed that the precision and accuracy of parameter estimates are acceptable. Based on the PKPD analysis, the recommended oral dose of haloperidol to achieve a 30% reduction in PANSS score from baseline is 5.6 mg/d, and the corresponding steady-state effective plasma haloperidol exposure is 2.7 ng/mL. In conclusion, the developed model describes the time course of PANSS scores adequately, and a recommendation of haloperidol dose was derived for future antipsychotic drug trials.

Pharmacokinetic-pharmacodynamic modeling of antipsychotic drugs in patients with schizophrenia Part I: the use of PANSS total score and clinical utility.

BACKGROUND: To develop a pharmacokinetic-pharmacodynamic (PK-PD) model using individual-level data of Positive and Negative Syndrome Scale (PANSS) total score to characterize the antipsychotic drug effect taking into account the placebo effect and dropout rate. In addition, a clinical utility (CU) criterion that describes the usefulness of a drug therapy was calculated using the efficacy of the drug and dropout rates. METHODS: Data from 12 clinical trials in schizophrenia patients was used to quantify the effects of the antipsychotic drugs (APs), namely, haloperidol, risperidone, olanzapine, ziprasidone and paliperidone. Compartmental PK models were used to describe the time course of plasma drug concentrations. The combination of an Emax and the Weibull model was used to describe the drug and placebo effects. The steady-state drug concentrations were assumed to be the drivers of the exposure-response relationship. An exponential model was utilized to identify the predictors of probability of dropout. Simulations were performed to check the predictability of the model, and to calculate the CU of the drugs based on PANSS scores and dropout rates. RESULTS: The maximal drug effect (E(max)) was highest for olanzapine whilst it was lowest for ziprasidone. Higher observed PANSS scores resulted in a greater likelihood of dropout. Taking into account the efficacy and the drop-out rate, all APs possessed a comparable CU at the therapeutic doses. The resulting PK-PD model parameters were used to compute the effective concentration and dose required to produce a clinically meaningful 30% drop in PANSS score from the baseline. CONCLUSIONS: The developed PK-PD model and the associated CU score allow the evaluation of the time course of the PANSS scores of the different APs and a proper comparison of their clinically relevant treatments effects.

Pharmacokinetic-pharmacodynamic modelling of antipsychotic drugs in patients with schizophrenia: part II: the use of subscales of the PANSS score.

BACKGROUND AND OBJECTIVES: The superiority of atypical antipsychotics (also known as second-generation antipsychotics (SGAs)) over typical antipsychotics (first generation antipsychotics (FGAs)) for negative symptom control in schizophrenic patients is widely debated. The objective of this study was to characterize the time course of the scores of the 3 subscales (positive, negative, general) of the Positive and Negative Syndrome Scale (PANSS) after treatment of patients with antipsychotics, and to compare the control of negative symptom by SGAs versus a FGA (haloperidol) using pharmacokinetic and pharmacodynamic (PKPD) modelling. In addition, to obtain insight in the relationship between the clinical efficacy and the in vitro and in vivo receptor pharmacology profiles, the D2 and 5-HT2A receptor occupancy levels of antipsychotics were related to the effective concentrations. METHODS: The PKPD model structure developed earlier (part I) was used to quantify the drug effect using the 3 PANSS subscales. The maximum drug effect sizes (Emax) of oral SGAs (risperidone, olanzapine, ziprasidone, and paliperidone) across PANSS subscales were compared with that of haloperidol, while accounting for the placebo effect. Using the estimates of PKPD model parameters, the effective concentrations (Ceff) needed to achieve 30% reduction in the PANSS subscales were computed. Calculated effective concentrations were then correlated with receptor pharmacology profiles. RESULTS: Positive symptoms of schizophrenia responded well to all antipsychotics. Olanzapine showed a better effect towards negative symptoms than the other SGAs and haloperidol. Dropout modelling results showed that the probability of a patient dropping out from a trial was associated with all subscales, but was more strongly correlated with the positive subscale than with the negative or the general subscales. Our results suggest that different levels of D2 or 5-HT2A receptor occupancy are required to achieve improvement in PANSS subscales. CONCLUSIONS: This PKPD modelling approach can be helpful to differentiate the effect of antipsychotics across the different symptom domains of schizophrenia. Our analysis revealed that olanzapine seems to be superior in treating the negative symptoms compared to other non-clozapine SGAs. The relationship between receptor pharmacology profiles of the antipsychotics and their clinical efficacy is not yet fully understood.

Sensitivity of individual items of the Positive and Negative Syndrome Scale (PANSS) and items subgroups to differentiate between placebo and drug treatment in schizophrenia.

Lack of hard clinical endpoints is an essential problem in schizophrenia research. Disease state and treatment outcomes are measured using rating scales, e.g. Positive and Negative Syndrome Scale (PANSS). However, the PANSS score cannot always differentiate between placebo and drug treatment, even for established antipsychotics. The goal of this study was to identify the individual items of PANSS and subscales of selected items which are most sensitive to differentiate between placebo and drug effect. We analysed data from seven clinical trials of different antipsychotics. "Mini-PANSS" scales consisting of the most sensitive items were created and analysed statistically. The power of these scales to show a significant difference between placebo and drug treatment was compared with the power of total PANSS. Furthermore, pharmacokinetic-pharmacodynamic analysis was performed to determine which of these scales shows the highest drug effect on top of the placebo effect. The results reveal that all 30 items of the PANSS scale show a therapeutic drug effect. The magnitude of placebo effect was not predictive for the power to detect drug effect. Mini-PANSS scales consisting of items with the largest drug treatment response and the scale with the largest mean-to-SD ratio are somewhat better in differentiating between placebo and drug treatment than the total PANSS. However, the difference between these scales and total PANSS is small. Therefore, our analysis does not support replacement of the total PANSS by a reduced scale in the analysis of primary endpoints.

Modelling and simulation of placebo effect: application to drug development in schizophrenia.

High and variable placebo effect (PE) within and among clinical trials can substantially affect conclusions about the efficacy of new drugs in the treatment of schizophrenia and other neuropsychiatric disorders. In recent years, it has become increasingly difficult to prove drug efficacy against placebo, and one of the reasons is that the placebo response has increased over recent years. The increased placebo response over the years is partly explained by unidentified parallel interventions, patient factors, issues with trial designs, and regional variability or demographic differences. In addition, a nocebo effect, which is undesirable effects a subject manifests after receiving placebo, e.g. extrapyramidal side effects, in placebo arms of antipsychotic trials could also influence the PE and clinical trial outcomes. Placebo effects (PEs) are a natural phenomenon and cannot be avoided completely in clinical trials. However, accounting for the PE via mixed effects modelling approaches could reduce bias in quantifying the overall effect size of the drug treatment. This review article focuses on the PE and its impact on schizophrenia clinical trial outcomes. The authors briefly describe the factors that lead to high and variable PE. Next, pharmacometric approaches to account for the PE and dropouts in schizophrenia clinical trials are described. Finally, some points are provided that could be considered while designing and optimizing antipsychotic trials via simulation approaches.

Pharmacokinetic-pharmacodynamic modeling of the D2 and 5-HT (2A) receptor occupancy of risperidone and paliperidone in rats.

PURPOSE: A pharmacokinetic-pharmacodynamic (PK-PD) model was developed to describe the time course of brain concentration and dopamine D2 and serotonin 5-HT(2A) receptor occupancy (RO) of the atypical antipsychotic drugs risperidone and paliperidone in rats. METHODS: A population approach was utilized to describe the PK-PD of risperidone and paliperidone using plasma and brain concentrations and D2 and 5-HT(2A) RO data. A previously published physiology- and mechanism-based (PBPKPD) model describing brain concentrations and D2 receptor binding in the striatum was expanded to include metabolite kinetics, active efflux from brain, and binding to 5-HT(2A) receptors in the frontal cortex. RESULTS: A two-compartment model best fit to the plasma PK profile of risperidone and paliperidone. The expanded PBPKPD model described brain concentrations and D2 and 5-HT(2A) RO well. Inclusion of binding to 5-HT(2A) receptors was necessary to describe observed brain-to-plasma ratios accurately. Simulations showed that receptor affinity strongly influences brain-to-plasma ratio pattern. CONCLUSION: Binding to both D2 and 5-HT(2A) receptors influences brain distribution of risperidone and paliperidone. This may stem from their high affinity for D2 and 5-HT(2A) receptors. Receptor affinities and brain-to-plasma ratios may need to be considered before choosing the best PK-PD model for centrally active drugs.

Modelling and simulation of the Positive and Negative Syndrome Scale (PANSS) time course and dropout hazard in placebo arms of schizophrenia clinical trials.

BACKGROUND AND OBJECTIVES: The likelihood of detecting a therapeutic signal of an effective drug for schizophrenia is impeded by a high placebo effect and by high dropout of patients. Several unsuccessful trials of schizophrenia, at least partly due to highly variable placebo effects, have indicated the necessity for a robust methodology to evaluate such a placebo effect and reasons for dropout. Hence, the objectives of this analysis were to (i) develop a longitudinal placebo model that accounts for dropouts and predictors of the placebo effect, using the Positive and Negative Syndrome Scale (PANSS) score; (ii) compare the performance of empirical and semi-mechanistic placebo models; and (iii) compare different time-to-event (TTE) dropout modelling approaches used to account for dropouts. METHODS: The PANSS scores from 1436 individual patients were used to develop and validate a placebo model. This pooled dataset included 16 trials (conducted between 1989 and 2009), with different study durations, in both acute and chronic schizophrenic patients. A nonlinear mixed-effects modelling approach was employed, using NONMEM VII software. RESULTS: Among the different tested placebo models, the Weibull model and the indirect response model adequately described the PANSS data. Covariate analysis showed that the disease condition, study duration, study year, geographic region where the trial was conducted, and route of administration were important predictors for the placebo effect. All three parametric TTE dropout models, namely the exponential, Weibull and Gompertz models, described the probability of patients dropping out from a clinical trial equally well. The study duration and trial phase were found to be predictors for high dropout rates. Results of joint modelling of the placebo effect and dropouts indicated that the probability of patients dropping out is associated with an observed high PANSS score. The indirect response model was found to be a slightly better model than the Weibull placebo model to describe the time course of the PANSS score. CONCLUSIONS: Our modelling approach was shown to adequately simulate the longitudinal PANSS data and the dropout trends after placebo treatment. Data analyses suggest that the Weibull and indirect response models are more robust than other placebo models to describe the nonlinear trends in the PANSS score. The developed placebo models, accounting for dropouts and predictors of the placebo effect, could be a useful tool in the evaluation of new trial designs and for better quantification of antipsychotic drug effects.

Structural models describing placebo treatment effects in schizophrenia and other neuropsychiatric disorders.

Large variation in placebo response within and among clinical trials can substantially affect conclusions about the efficacy of new medications in psychiatry. Developing a robust placebo model to describe the placebo response is important to facilitate quantification of drug effects, and eventually to guide the design of clinical trials for psychiatric treatment via a model-based simulation approach. In addition, high dropout rates are very common in the placebo arm of psychiatric clinical trials. While developing models to evaluate the effect of placebo response, the data from patients who drop out of the trial should be considered for accurate interpretation of the results. The objective of this paper is to review the various empirical and semi-mechanistic models that have been used to quantify the placebo response in schizophrenia trials. Pros and cons of each placebo model are discussed. Additionally, placebo models used in other neuropsychiatric disorders like depression, Alzheimer's disease and Parkinson's disease are also reviewed with the objective of finding those placebo models that could be useful for clinical studies of both acute and chronic schizophrenic disease conditions. Better understanding of the patterns of dropout and the factors leading to dropouts are crucial in identifying the true placebo response. We therefore also review dropout models that are used in the development of models for treatment effects and in the optimization of clinical trials by simulation approaches. The use of an appropriate modelling strategy that is capable of identifying the potential sources of variable placebo responses and dropout rates is recommended for improving the sensitivity in discriminating between the effects of active treatment and placebo.

Mechanism-based pharmacokinetic-pharmacodynamic modeling of the dopamine D2 receptor occupancy of olanzapine in rats.

PURPOSE: A mechanism-based PK-PD model was developed to predict the time course of dopamine D(2) receptor occupancy (D(2)RO) in rat striatum following administration of olanzapine, an atypical antipsychotic drug. METHODS: A population approach was utilized to quantify both the pharmacokinetics and pharmacodynamics of olanzapine in rats using the exposure (plasma and brain concentration) and D(2)RO profile obtained experimentally at various doses (0.01-40 mg/kg) administered by different routes. A two-compartment pharmacokinetic model was used to describe the plasma pharmacokinetic profile. A hybrid physiology- and mechanism-based model was developed to characterize the D(2) receptor binding in the striatum and was fitted sequentially to the data. The parameters were estimated using nonlinear mixed-effects modeling . RESULTS: Plasma, brain concentration profiles and time course of D(2)RO were well described by the model; validity of the proposed model is supported by good agreement between estimated association and dissociation rate constants and in vitro values from literature. CONCLUSION: This model includes both receptor binding kinetics and pharmacokinetics as the basis for the prediction of the D(2)RO in rats. Moreover, this modeling framework can be applied to scale the in vitro and preclinical information to clinical receptor occupancy.

Are autosomal sex-determining factors of the housefly (Musca domestica) spreading north?

Multiple sex-determining factors have been found in natural populations of the housefly, Musca domestica. Their distribution seems to follow a geographical cline. The 'standard' system, with a male-determining factor, M, located on the Y chromosome, prevails at higher latitudes and altitudes. At lower latitudes and altitudes M factors have also been found on any of the five autosomes. Such populations often also harbour a dominant autosomal factor, FD, which induces female development even in the presence of several M factors. Autosomal M factors were first observed some 50 years ago. It has been hypothesized that following their initial appearance, they are spreading northwards, replacing the standard XY system, but this has never been systematically investigated. To scrutinize this hypothesis, we here compare the current distribution of autosomal M factors in continental Europe, on a transect running from Germany to southern Italy, with the distribution reported 25 years ago. Additionally, we analysed the frequencies of the FD factor, which has not been done before for European populations. In contrast to earlier predictions, we do not find a clear change in the distribution of sex-determining factors: as 25 years ago, only the standard XY system is present in the north, while autosomal M factors and the FD factor are prevalent in Italy. We discuss possible causes for this apparently stable polymorphism.

Good genes and the maternal effects of polyandry on offspring reproductive success in the bulb mite.

Genetic benefits are potentially the most robust explanation of the controversial issue of evolutionary maintenance of polyandry, but the unambiguous demonstration of such benefits has been hindered by the possibility of their confusion with maternal effects. Previous research has shown that polyandrous bulb mite females produce daughters with higher fecundity than monandrous females. Here, we investigate whether this effect arises because polyandrous females invest more in their offspring, or because their offspring inherit 'good genes' from their fathers. Females were mated with either one or four (different) males. However, by sterilizing three of the four males with ionizing radiation, we eliminated any chance of sexual selection (in the polyandrous treatment) so that any differences in the female mating regimes must have been owing to maternal effects. Polyandry had no significant effect on daughter fecundity, thus indicating that any previously documented effects must have been genetic. This was further supported by a significant association between fathers' offensive sperm-competitive ability and the fecundity of their daughters. The association with fathers' sperm defensive ability was not significant, and neither was the association between fathers' sperm competitiveness and sons' reproductive success. However, sons of polyandrous females had lower reproductive success than sons of monandrous females. This shows that the maternal effects of polyandry should be taken into account whenever its costs and benefits are being considered.