An integrative model of Parkinson's disease treatment including levodopa pharmacokinetics, dopamine kinetics, basal ganglia neurotransmission and motor action throughout disease progression

Valence-dependent dopaminergic modulation during reversal learning in Parkinson's disease: A neurocomputational approach

Reinforcement learning, crucial for behavior in dynamic environments, is driven by rewards and punishments, modulated by dopamine (DA) changes. This study explores the dopaminergic system's influence on learning, particularly in Parkinson's disease (PD), where medication leads to impaired adaptability. Highlighting the role of tonic DA in signaling the valence of actions, this research investigates how DA affects response vigor and decision-making in PD. DA not only influences reward and punishment learning but also indicates the cognitive effort level and risk propensity in actions, which are essential for understanding and managing PD symptoms. In this work, we adapt our existing neurocomputational model of basal ganglia (BG) to simulate two reversal learning tasks proposed by Cools et al. We first optimized a Hebb rule for both probabilistic and deterministic reversal learning, conducted a sensitivity analysis (SA) on parameters related to DA effect, and compared performances between three groups: PD-ON, PD-OFF, and control subjects. In our deterministic task simulation, we explored switch error rates after unexpected task switches and found a U-shaped relationship between tonic DA levels and switch error frequency. Through SA, we classify these three groups. Then, assuming that the valence of the stimulus affects the tonic levels of DA, we were able to reproduce the results by Cools et al. As for the probabilistic task simulation, our results are in line with clinical data, showing similar trends with PD-ON, characterized by higher tonic DA levels that are correlated with increased difficulty in both acquisition and reversal tasks. Our study proposes a new hypothesis: valence, signaled by tonic DA levels, influences learning in PD, confirming the uncorrelation between phasic and tonic DA changes. This hypothesis challenges existing paradigms and opens new avenues for understanding cognitive processes in PD, particularly in reversal learning tasks.

Computational neurosciences and quantitative systems pharmacology: a powerful combination for supporting drug development in neurodegenerative diseases

Successful clinical development of new therapeutic interventions is notoriously difficult, especially in neurodegenerative diseases, where predictive biomarkers are scarce and functional improvement is often based on patient's perception, captured by structured interviews. As a consequence, mechanistic modeling of the processes relevant to therapeutic interventions in CNS disorders has been lagging behind other disease indications, probably because of the perceived complexity of the brain. However in this report, we develop the argument that a combination of Computational Neurosciences and Quantitative Systems Pharmacology (QSP) modeling of molecular pathways is a powerful simulation tool to enhance the probability of successful drug development for neurodegenerative diseases. Computational Neurosciences aims to predict action potential dynamics and neuronal circuit activation that are ultimately linked to behavioral changes and clinically relevant functional outcomes. These processes can not only be affected by the disease state, but also by common genotype variants on neurotransmitter-related proteins and the psycho-active medications often prescribed in these patient populations. Quantitative Systems Pharmacology (QSP) modeling of molecular pathways allows to simulate key pathological drivers of dementia, such as protein aggregation and neuroinflammatory responses. They often impact neurotransmitter homeostasis and voltage-gated ion-channels or lead to mitochondrial dysfunction, ultimately leading to changes in action potential dynamics and clinical readouts. Combining these two modeling approaches can lead to better actionable understanding of the many non-linear pharmacodynamic processes active in the human diseased brain. Practical applications include a rational selection of the optimal doses in combination therapies, identification of subjects more likely to respond to treatment, a more balanced stratification of treatment arms in terms of comedications, disease status and common genotype variants and re-analysis of small clinical trials to uncover a possible clinical signal. Ultimately this will lead to a higher success rate of bringing new therapeutics to the right patient populations.

A comparative study of automatic vowel articulation index and auditory-perceptual assessments of speech intelligibility in Parkinson's disease

PURPOSE

The purpose of this study was to analyse the relationship between automatic vowel articulation index (aVAI) and direct magnitude estimation (DME) among speakers with Parkinson's disease (PD) and healthy controls. We further analysed the potential of aVAI to serve as an objective measure of speech impairment in the clinical setting.

METHOD

Speech samples from native Finnish speakers were utilised. Expert raters utilised DME to scale the intelligibility of speech samples. aVAI scores for PD speakers and healthy control speakers were analysed in relationship to DME speech intelligibility ratings and, among PD speakers, disease stage utilising nonparametric statistical analysis.

RESULT

Mean DME intelligibility ratings were lower among PD speakers compared to healthy controls. Mean aVAI scores were nearly the same between speaker groups. DME intelligibility ratings and aVAI were strongly correlated within the PD speaker group. aVAI and DME intelligibility ratings were moderately correlated with disease stage as measured by the Hoehn and Yahr scale.

CONCLUSION

aVAI was observed to be a promising tool for analysing vowel articulation in PD speakers. Further research is warranted on the application of aVAI as an objective measure of severity of speech impairment in the clinical setting, with varying patient populations and speech samples.

Therapeutic drug monitoring in Parkinson's disease

A patient-tailored therapy of the heterogeneous, neuropsychiatric disorder of Parkinson's disease (PD) aims to improve dopamine sensitive motor symptoms and associated non-motor features. A repeated, individual adaptation of dopamine substituting compounds is required throughout the disease course due to the progress of neurodegeneration. Therapeutic drug monitoring of dopamine substituting drugs may be an essential tool to optimize drug applications. We suggest plasma determination of levodopa as an initial step. The complex pharmacology of levodopa is influenced by its short elimination half-life and the gastric emptying velocity. Both considerably contribute to the observed variability of plasma concentrations of levodopa and its metabolite 3-O-methyldopa. These amino acids compete with other aromatic amino acids as well as branched chain amino acids on the limited transport capacity in the gastrointestinal tract and the blood brain barrier. However, not much is known about plasma concentrations of levodopa and other drugs/drug combinations in PD. Some examples may illustrate this lack of knowledge: Levodopa measurements may allow further insights in the phenomenon of inappropriate levodopa response. They may result from missing compliance, interactions e.g. with treatments for other mainly age-related disorders, like hypertension, diabetes, hyperlipidaemia, rheumatism or by patients themselves independently taken herbal medicines. Indeed, uncontrolled combination of compounds for accompanying disorders as given above with PD drugs might increase the risk of side effects. Determination of other drugs used to treat PD in plasma such as dopamine receptor agonists, amantadine and inhibitors of catechol-O-methyltransferase or monoamine oxidase B may refine and improve the value of calculations of levodopa equivalents. How COMT-Is change levodopa plasma concentrations? How other dopaminergic and non-dopaminergic drugs influence levodopa levels? Also, delivery of drugs as well as single and repeated dosing and continuous levodopa administrations with a possible accumulation of levodopa, pharmacokinetic behaviour of generic and branded compounds appear to have a marked influence on efficacy of drug treatment and side effect profile. Their increase over time may reflect progression of PD to a certain degree. Therapeutic drug monitoring in PD is considered to improve the therapeutic efficacy in the course of this devastating neurologic disorder and therefore is able to contribute to the patients' precision medicine. State-of-the-art clinical studies are urgently needed to demonstrate the usefulness of TDM for optimizing the treatment of PD.

Brain-inspired biomimetic robot control: a review

Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots, pose a challenging control problem due to their high dimensionality and heavy non-linearities. Conventional model-based feedback controllers demonstrate robustness and stability but struggle to cope with the escalating system design and tuning complexity accompanying larger dimensions. In contrast, data-driven methods such as artificial neural networks excel at representing high-dimensional data but lack robustness, generalization, and real-time adaptiveness. In response to these challenges, researchers are directing their focus to biological paradigms, drawing inspiration from the remarkable control capabilities inherent in the human body. This has motivated the exploration of new control methods aimed at closely emulating the motor functions of the brain given the current insights in neuroscience. Recent investigation into these Brain-Inspired control techniques have yielded promising results, notably in tasks involving trajectory tracking and robot locomotion. This paper presents a comprehensive review of the foremost trends in biomimetic brain-inspired control methods to tackle the intricacies associated with controlling complex robotic systems.

3D printing combined with biopredictive dissolution and PBPK/PD modeling optimization and personalization of pharmacotherapy: Are we there yet?

Precision medicine requires selecting the appropriate dosage regimen for a patient using the right drug, at the right time. Model-Informed Precision Dosing (MIPD) is a concept suggesting utilization of model-based prediction methods for optimizing the treatment benefit-harm balance, based on individual characteristics of the patient, disease, treatment method, and other factors. Here, we discuss a theoretical workflow comprising several elements, beginning from the physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models, through 3D printed tablets with the model proposed dose, information range and flow, and the patient themselves. We also describe each of these elements, and the connection between them, highlighting challenges and potential obstacles.

The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach

Motor learning involves a widespread brain network including the basal ganglia, cerebellum, motor cortex, and brainstem. Despite its importance, little is known about how this network learns motor tasks and which role different parts of this network take. We designed a systems-level computational model of motor learning, including a cortex-basal ganglia motor loop and the cerebellum that both determine the response of central pattern generators in the brainstem. First, we demonstrate its ability to learn arm movements toward different motor goals. Second, we test the model in a motor adaptation task with cognitive control, where the model replicates human data. We conclude that the cortex-basal ganglia loop learns via a novelty-based motor prediction error to determine concrete actions given a desired outcome, and that the cerebellum minimizes the remaining aiming error.

Opicapone Pharmacokinetics and Effects on Catechol- O -Methyltransferase Activity and Levodopa Pharmacokinetics in Patients With Parkinson Disease Receiving Carbidopa/Levodopa

OBJECTIVES

Levodopa (LD) administered with dopa decarboxylase inhibitor is predominantly metabolized in the periphery by catechol- O -methyltransferase (COMT) to 3- O -methyldopa (3-OMD). Catechol- O -methyltransferase inhibition can improve treatment outcomes by decreasing variability in circulating LD concentrations. Opicapone is a once-daily COMT inhibitor approved in the US adjunctive to carbidopa (CD)/LD in patients with Parkinson disease experiencing "OFF" episodes. This study aimed to evaluate the pharmacokinetics and pharmacodynamics of once-daily opicapone 50 mg adjunctive to CD/LD in patients with stable Parkinson disease.

METHODS

Once-daily opicapone 50 mg was administered the evenings of days 1 to 14. Participants were randomized to receive CD/LD (25/100 mg) every 3 or 4 hours (Q3H or Q4H). Participants received Q3H or Q4H CD/LD on days 1, 2, and 15 and their usual CD/LD regimen on other days. Serial blood samples were collected to determine plasma opicapone, LD, and 3-OMD concentrations and erythrocyte soluble COMT (S-COMT) activity. The effects of opicapone on S-COMT, LD, and 3-OMD were assessed. Mean (SD) values are presented.

RESULTS

Sixteen participants were enrolled. At steady-state (day 14), opicapone Cmax (peak plasma concentration) and AUC 0-last (area under the curve-time curve) were 459 ± 252 ng/mL and 2022 ± 783 ng/mL·h, respectively. Maximum COMT inhibition was 83.4 ± 4.9% of baseline on day 14. After opicapone administration, LD total AUC, peak concentration, and trough concentration increased; peak-to-trough fluctuation index decreased. Correspondingly, 3-OMD total AUC, peak concentration, and trough concentration decreased.

CONCLUSIONS

Adding once-daily opicapone 50 mg to LD resulted in marked and extended COMT inhibition, which increased systemic exposure to LD. These changes translated into higher trough concentrations and decreased peak-to-trough fluctuations for LD.

Hallmarks of neurodegenerative disease: A systems pharmacology perspective

Age-related central neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are a rising public health concern and have been plagued by repeated drug development failures. The complex nature and poor mechanistic understanding of the etiology of neurodegenerative diseases has hindered the discovery and development of effective disease-modifying therapeutics. Quantitative systems pharmacology models of neurodegeneration diseases may be useful tools to enhance the understanding of pharmacological intervention strategies and to reduce drug attrition rates. Due to the similarities in pathophysiological mechanisms across neurodegenerative diseases, especially at the cellular and molecular levels, we envision the possibility of structural components that are conserved across models of neurodegenerative diseases. Conserved structural submodels can be viewed as building blocks that are pieced together alongside unique disease components to construct quantitative systems pharmacology (QSP) models of neurodegenerative diseases. Model parameterization would likely be different between the different types of neurodegenerative diseases as well as individual patients. Formulating our mechanistic understanding of neurodegenerative pathophysiology as a mathematical model could aid in the identification and prioritization of drug targets and combinatorial treatment strategies, evaluate the role of patient characteristics on disease progression and therapeutic response, and serve as a central repository of knowledge. Here, we provide a background on neurodegenerative diseases, highlight hallmarks of neurodegeneration, and summarize previous QSP models of neurodegenerative diseases.

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.

DOPA Homeostasis by Dopamine: A Control-Theoretic View

Dopamine (DA) is an important signal mediator in the brain as well as in the periphery. The term "dopamine homeostasis" occasionally found in the literature refers to the fact that abnormal DA levels can be associated with a variety of neuropsychiatric disorders. An analysis of the negative feedback inhibition of tyrosine hydroxylase (TH) by DA indicates, with support from the experimental data, that the TH-DA negative feedback loop has developed to exhibit 3,4-dihydroxyphenylalanine (DOPA) homeostasis by using DA as a derepression regulator. DA levels generally decline when DOPA is removed, for example, by increased oxidative stress. Robust DOPA regulation by DA further implies that maximum vesicular DA levels are established, which appear necessary for a reliable translation of neural activity into a corresponding chemical transmitter signal. An uncontrolled continuous rise (windup) in DA occurs when Levodopa treatment exceeds a critical dose. Increased oxidative stress leads to the successive breakdown of DOPA homeostasis and to a corresponding reduction in DA levels. To keep DOPA regulation robust, the vesicular DA loading requires close to zero-order kinetics combined with a sufficiently high compensatory flux provided by TH. The protection of DOPA and DA due to a channeling complex is discussed.

Personalized Medicine to Improve Treatment of Dopa-Responsive Dystonia-A Focus on Tyrosine Hydroxylase Deficiency

Dopa-responsive dystonia (DRD) is a rare movement disorder associated with defective dopamine synthesis. This impairment may be due to the fact of a deficiency in GTP cyclohydrolase I (GTPCHI, GCH1 gene), sepiapterin reductase (SR), tyrosine hydroxylase (TH), or 6-pyruvoyl tetrahydrobiopterin synthase (PTPS) enzyme functions. Mutations in GCH1 are most frequent, whereas fewer cases have been reported for individual SR-, PTP synthase-, and TH deficiencies. Although termed DRD, a subset of patients responds poorly to L-DOPA. As this is regularly observed in severe cases of TH deficiency (THD), there is an urgent demand for more adequate or personalized treatment options. TH is a key enzyme that catalyzes the rate-limiting step in catecholamine biosynthesis, and THD patients often present with complex and variable phenotypes, which results in frequent misdiagnosis and lack of appropriate treatment. In this expert opinion review, we focus on THD pathophysiology and ongoing efforts to develop novel therapeutics for this rare disorder. We also describe how different modeling approaches can be used to improve genotype to phenotype predictions and to develop in silico testing of treatment strategies. We further discuss the current status of mathematical modeling of catecholamine synthesis and how such models can be used together with biochemical data to improve treatment of DRD patients.