Modélisation pharmacocinétique du rythme circadien

L’être humain est organisé selon une horloge interne d’une période d’environ 24 heures. La pharmacocinétique de certaines classes de médicaments est donc influencée par le rythme circadien. En effet, l’aire sous la courbe de la concentration en médicament en fonction du temps, la concentration maximale en médicament et le temps auquel on obtient la concentration maximale peuvent varier en fonction de l’heure à laquelle a été consommé le médicament. Le but de ce travail est de modéliser la variation de la concentration maximale de ces médicaments selon le moment de la journée auquel ils sont pris. On étudie d’abord un modèle présenté par Godfrey permettant de trouver la concen- tration en médicament en fonction du temps et tenant compte des variations circadiennes. Ce modèle ne permet pas d’illustrer les variations dans la concentration maximale selon le moment de la journée auquel le médicament est pris. Un nouveau modèle à deux com- partiments sera donc développé pour les trois modes d’absorption (orale, intraveineuse, intraveineuse bolus). Les systèmes d’équations différentielles résultants seront étudiés. L’effet de la variation des paramètres de phase sur la concentration maximale sera aussi étudié. La preuve de l’existence des solutions, de leur unicité et de leur positivité sera faite en annexe.Humans are organised according to an internal clock with a period of approximatively 24 hours. The pharmacokinetic of several classes of drugs are then influenced by circadian rhythms. Indeed, the area under the curve (of the drug concentration as a function of time), the maximal concentration and the time to maximal concentration can change according to the time at which the drug is taken. The objective of this present work is to find a model to represent the variations in the maximal drug concentration according to the absorption’s time. We first study a model presented by Godfrey. It allows to find the drug concentration as a function of time while taking into account circadian rhythms. Unfortunately, this model could not represent the variations in the maximal concentration according to the time at which the drug is taken. We developed a new two-compartmental model for the three ways of absorption (oral, intravenous and intravenous bolus). The resulting systems of ordinary differential equations will be studied. The effect of the phase parameters on the maximal concen- tration will also be studied. Finally, the proof of well-poseness of the model will be developed in the Annex

Phasic Dopamine Changes and Hebbian Mechanisms during Probabilistic Reversal Learning in Striatal Circuits: A Computational Study

Cognitive flexibility is essential to modify our behavior in a non-stationary environment and is often explored by reversal learning tasks. The basal ganglia (BG) dopaminergic system, under a top-down control of the pre-frontal cortex, is known to be involved in flexible action selection through reinforcement learning. However, how adaptive dopamine changes regulate this process and learning mechanisms for training the striatal synapses remain open questions. The current study uses a neurocomputational model of the BG, based on dopamine-dependent direct (Go) and indirect (NoGo) pathways, to investigate reinforcement learning in a probabilistic environment through a task that associates different stimuli to different actions. Here, we investigated: the efficacy of several versions of the Hebb rule, based on covariance between pre- and post-synaptic neurons, as well as the required control in phasic dopamine changes crucial to achieving a proper reversal learning. Furthermore, an original mechanism for modulating the phasic dopamine changes is proposed, assuming that the expected reward probability is coded by the activity of the winner Go neuron before a reward/punishment takes place. Simulations show that this original formulation for an automatic phasic dopamine control allows the achievement of a good flexible reversal even in difficult conditions. The current outcomes may contribute to understanding the mechanisms for active control of dopamine changes during flexible behavior. In perspective, it may be applied in neuropsychiatric or neurological disorders, such as Parkinson's or schizophrenia, in which reinforcement learning is impaired

A mechanistic model of ADHD as resulting from dopamine phasic/tonic imbalance during reinforcement learning

Attention deficit hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in children. Although the involvement of dopamine in this disorder seems to be established, the nature of dopaminergic dysfunction remains controversial. The purpose of this study was to test whether the key response characteristics of ADHD could be simulated by a mechanistic model that combines a decrease in tonic dopaminergic activity with an increase in phasic responses in cortical-striatal loops during learning reinforcement. To this end, we combined a dynamic model of dopamine with a neurocomputational model of the basal ganglia with multiple action channels. We also included a dynamic model of tonic and phasic dopamine release and control, and a learning procedure driven by tonic and phasic dopamine levels. In the model, the dopamine imbalance is the result of impaired presynaptic regulation of dopamine at the terminal level. Using this model, virtual individuals from a dopamine imbalance group and a control group were trained to associate four stimuli with four actions with fully informative reinforcement feedback. In a second phase, they were tested without feedback. Subjects in the dopamine imbalance group showed poorer performance with more variable reaction times due to the presence of fast and very slow responses, difficulty in choosing between stimuli even when they were of high intensity, and greater sensitivity to noise. Learning history was also significantly more variable in the dopamine imbalance group, explaining 75% of the variability in reaction time using quadratic regression. The response profile of the virtual subjects varied as a function of the learning history variability index to produce increasingly severe impairment, beginning with an increase in response variability alone, then accumulating a decrease in performance and finally a learning deficit. Although ADHD is certainly a heterogeneous disorder, these results suggest that typical features of ADHD can be explained by a phasic/tonic imbalance in dopaminergic activity alone

A mechanistic model of ADHD as resulting from dopamine phasic/tonic imbalance during reinforcement learning

Attention deficit hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in children. Although the involvement of dopamine in this disorder seems to be established, the nature of dopaminergic dysfunction remains controversial. The purpose of this study was to test whether the key response characteristics of ADHD could be simulated by a mechanistic model that combines a decrease in tonic dopaminergic activity with an increase in phasic responses in cortical-striatal loops during learning reinforcement. To this end, we combined a dynamic model of dopamine with a neurocomputational model of the basal ganglia with multiple action channels. We also included a dynamic model of tonic and phasic dopamine release and control, and a learning procedure driven by tonic and phasic dopamine levels. In the model, the dopamine imbalance is the result of impaired presynaptic regulation of dopamine at the terminal level. Using this model, virtual individuals from a dopamine imbalance group and a control group were trained to associate four stimuli with four actions with fully informative reinforcement feedback. In a second phase, they were tested without feedback. Subjects in the dopamine imbalance group showed poorer performance with more variable reaction times due to the presence of fast and very slow responses, difficulty in choosing between stimuli even when they were of high intensity, and greater sensitivity to noise. Learning history was also significantly more variable in the dopamine imbalance group, explaining 75% of the variability in reaction time using quadratic regression. The response profile of the virtual subjects varied as a function of the learning history variability index to produce increasingly severe impairment, beginning with an increase in response variability alone, then accumulating a decrease in performance and finally a learning deficit. Although ADHD is certainly a heterogeneous disorder, these results suggest that typical features of ADHD can be explained by a phasic/tonic imbalance in dopaminergic activity alone

Étude par pharmacologie quantitative du système dopaminergique des ganglions de la base pour l’optimisation de la pharmacothérapie. Modèle unificateur pour la maladie de Parkinson et le TDAH

La dopamine est un neurotransmetteur important dans le fonctionnement des ganglions de la base, région du cerveau impliquée dans la fonction motrice et l’apprentissage. Un dérèglement de la dynamique de la dopamine peut être à l’origine de différentes pathologies neurologiques, telles que la maladie de Parkinson et le trouble de déficit de l’attention avec ou sans hyperactivité (TDAH). La lévodopa, un précurseur de la dopamine, est utilisée pour réduire les symptômes associés à la maladie de Parkinson, sans action directe sur ses causes. La lévodopa est très efficace au début de la maladie, mais la durée de son effet ainsi que son index thérapeutique diminuent avec la progression de la dénervation induite par la maladie. Ces changements compliquent considérablement l’optimisation des régimes posologiques. Le méthylphénidate, quant à lui, est administré pour réduire les symptômes du TDAH et agit entre autres en bloquant la recapture de la dopamine. Bien que les données confirment une certaine implication de la dopamine dans le TDAH, son étiologie exacte demeure inconnue. Peu d’études ont cerné l’effet de la lévodopa sur le système dopaminergique des ganglions de la base et son évolution avec la progression de la maladie. Aussi, bien que le TDAH ait suscité beaucoup d’intérêt, rares sont les études quantitatives de nature mécanistiques sur le sujet. L’approche de modélisation mathématique utilisée dans cette thèse s’inscrit dans un effort global visant l’optimisation de la lévodopa et du méthylphénidate, appuyé par l’élucidation des mécanismes impliqués dans la maladie de Parkinson et dans le TDAH. En adoptant une approche de pharmacologie quantitative des systèmes (QSP), nous avons développé un modèle intégratif du système dopaminergique des ganglions de la base, avec l’objectif d’élucider les mécanismes impliqués, d’évaluer l’impact de la dopamine chez dessujets souffrant de Parkinson ou de TDAH, et recevant ou non un traitement, et enfin de guider objectivement l’exercice d’optimisation des régimes posologiques. À notre connaissance, c’est le premier cadre unificateur de modélisation qui s’adresse à ces deux pathologies. Le modèle développé dans cette thèse est composé de trois sous-modèles : le premier décrit la pharmacocinétique du médicament concerné, soit la lévodopa ou le méthylphénidate ; le deuxième exprime mathématiquement les différents mécanismes impliqués dans la dynamique de la dopamine ; le troisième représente la complexité de la neurotransmission dans les ganglions de la base. Avec des adaptations appropriées, nous avons appliqué ce même modèle au contexte de la maladie de Parkinson et au TDAH, ainsi qu’à leurs thérapies respectives. Pour représenter physiologiquement la maladie de Parkinson, nous avons intégré dans le modèle l’évolution de la perte neuronale ainsi que les différents mécanismes de compensation qui en résultent. La fréquence de tapotement des doigts est utilisée comme mesure clinique de la bradykinésie, définie comme le ralentissement des mouvements chez les patients parkinsoniens. Le modèle développé se base sur les connaissances actuelles de la pathophysiologie et pharmacologie du Parkinson, assurant ainsi sa validité en comparaison à des observations expérimentales et cliniques. Ensuite, à l’aide de ce modèle, les relations non-linéaires entre la concentration plasmatique de lévodopa, la concentration en dopamine dans le cerveau et la réponse à une tâche motrice sont étudiées. Le rétrécissement de l’index thérapeutique de la lévodopa au cours de la progression de la maladie dû à ces non-linéarités est investigué. Enfin, pour assurer l’aspect translationnel de notre approche, nous avons développé une application web à laquelle ce modèle a été intégré. Cette application sert de preuve de concept à un outil facilitant l’optimisation et l’individualisation des régimes posologiques. Pour l’étude du TDAH, nous avons adapté le modèle du système dopaminergique en y intégrant la libération tonique et phasique de la dopamine, cette dernière se produisant durant une tâche d’apprentissage par renforcement. Des individus virtuels ont été créés avec et sans déséquilibre du ratio tonique/phasique de la dopamine. En simulant une tâche de réponse à des stimuli dans un contexte de déséquilibre de la dopamine, le modèle nous a permis d’observer des symptômes similiaires à ceux de patients réels souffrant de TDAH. Finalement, la réponse au méthylphénidate résultant de l’inhibition de la recapture de la dopamine, à travers différents scénarios d’apprentissage a aussi été étudiée. Le développement d’une métrique nous a permis de différencier les répondants des non-répondants, et ainsi de mettre en évidence l’implication possible d’un apprentissage excessif chez les nonrépondants. Une meilleure compréhension de la réponse au méthylphénidate permettrait d’éviter la surmédication chez les non-répondants et d’aider les cliniciens dans leur pratique. Malgré la complexité du système dopaminergique et des traitements associés, cette thèse est un pas en avant dans la compréhension des mécanismes sous-jacents et de leur implication dans la thérapie. Ces avancées ont été réalisées en adoptant une approche de pharmacologie quantitative des systèmes, associée à une modélisation neurocomputationnelle du domaine du génie électrique, et complétée par un aspect de transfert au chevet du patient. Ce n’est qu’en transcendant ainsi les frontières disciplinaires qu’une visée aussi globale et intégrative est possible, afin de faire face aux défis multidimensionnels du système de la santé.Dopamine is an important neurotransmitter of the basal ganglia, a region of the brain involved in motor function and learning. Disruption of dopamine dynamics can cause various neurological conditions, such as Parkinson’s disease and attention deficit hyperactivity disorder (ADHD). Levodopa, a dopamine precursor, is used to reduce the symptoms associated with Parkinson’s disease, without directly alleviating its causes. Levodopa is very effective in the early stages of the disease, but its effect duration along with its therapeutic index decrease with disease-induced denervation. These modifications further challenge determination of optimal dosing regimens of levodopa. In the case of ADHD, methylphenidate is administered to reduce its symptoms by, among other things, blocking dopamine recapture. Although evidence supports involvement of dopamine in ADHD, its exact etiology remains unknown. Few studies have investigated the effect of levodopa on the basal ganglia dopaminergic system and how it evolves with disease progression. Also, although ADHD has received a lot of interest, few quantitative studies of a mechanistic nature have been conducted on the subject. The mathematical modeling approach used in this thesis is part of an overall effort to optimize levodopa and methylphenidate, supported by the elucidation of the mechanisms involved in Parkinson’s disease and ADHD. Using a quantitative systems pharmacology (QSP) approach, we have developed an integrative model of the basal ganglia dopaminergic system, with the objective of elucidating the mechanisms involved, assessing the impact of dopamine in subjects with Parkinson’s or ADHD, with and without treatment, and objectively guiding the dosing regimens optimization. To the best of our knowledge, this is the first unifying modeling framework that addresses at the same time these two pathologies and their therapies. The model developed in this thesis includes three sub-models: the first one describes the drug pharmacokinetics, either levodopa or methylphenidate; the second one translates mathematically the different mechanisms involved in the dopamine dynamics; the third one is a computational representation of the complexity of neurotransmission in the basal ganglia. With appropriate adaptations, we have applied this same model to the context of Parkinson’s disease and ADHD, as well as to their respective pharmacotherapies. In order to physiologically represent Parkinson’s disease, we have integrated the denervation process in the model as well as the resulting compensation mechanisms. The finger tapping frequency is used as a clinical endpoint of bradykinesia, defined as the slowing of movements. The developed model is based on up-to-date knowledge of the pathophysiology and pharmacology of Parkinson’s disease, thus ensuring its validity in comparison with experimental and clinical observations. Using this model, the non-linear relationships between plasma levodopa concentration, dopamine concentration in the brain and response to a motor task were studied. The narrowing of levodopa therapeutic index during the progression of the disease due to these non-linearities was investigated. Finally, to ensure the translational aspect of our approach, we developed a web application in which this model was integrated. This application serves as a proof of concept for a tool aimed to facilitate the optimization and individualization of dosing regimens. For the study of ADHD, we adapted the developed model by integrating tonic and phasic dopamine release, the latter occurring during a reinforcement learning task. Virtual individuals were created with and without dopamine imbalance in the tonic/phasic ratio. By simulating a stimulus-response task, we observe ADHD-like symptoms among virtual patients with dopamine imbalance. Finally, the response to methylphenidate resulting from dopamine recapture inhibition, through different learning scenarios, was also studied. The development of a metric allowed us to differentiate responders from non-responders, and thus to highlight the possible implication of excessive learning in non-responders. A better understanding of methylphenidate response would help avoid overmedication in non-responders and assist clinicians in their practice. Despite the complexity of the dopaminergic system and its associated therapies, this thesis is a step forward in understanding the underlying mechanisms and their involvement in pharmacotherapy. These advances were achieved by adopting a quantitative systems pharmacology approach, combined with neurocomputational modeling borrowed from the electrical engineering field, and complemented by a translational bedside aspect. It is only by transcending disciplinary boundaries and adopting such an integrative approach that this ultimate goal of having a real impact on the multifaceted health system is possible