| Literature DB >> 35572924 |
Krzysztof Sadowski1, Kamil Sijko1,2, Dorota Domańska-Pakieła1, Julita Borkowska1, Dariusz Chmielewski1, Agata Ulatowska1, Sergiusz Józwiak1,3, Katarzyna Kotulska1.
Abstract
Background: Epilepsy develops in 70-90% of children with Tuberous Sclerosis Complex (TSC) and is often resistant to medication. Treatment with mTOR pathway inhibitors is an important therapeutic option in drug-resistant epilepsy associated with TSC. Our study evaluated the antiepileptic effect of rapamycin in the pediatric population of patients diagnosed with TSC.Entities:
Keywords: epilepsy; mTOR inhibitors; rapamycin; sirolimus; tuberous sclerosis
Year: 2022 PMID: 35572924 PMCID: PMC9100395 DOI: 10.3389/fneur.2022.704978
Source DB: PubMed Journal: Front Neurol ISSN: 1664-2295 Impact factor: 4.003
Baseline demographics and medical history characteristics.
| Age at inclusion [years, mean (range)] | 5.2 (0.9–14.3) |
| Male/female [number, (percentage)] | 16/16 (50/50) |
| Mutation TSC1/TSC2/NMI/no data [number (percentage)] | 3/18/1/10 (9/56/3/32) |
| Age at first seizure [months, mean (range)] | 6.6 (0–30) |
| Seizure frequency per day at baseline [mean (range)] | 8 (1–10) |
| No. of AEDs at baseline [median (range)] | 3 (1–4) |
| AEDs usage at baseline [no of patients] | |
| VGB | 28 |
| VPA | 23 |
| TPM | 10 |
| LTG | 9 |
| LEV | 8 |
| CLB | 5 |
| OXC | 1 |
Data in brackets are range (“-“) and % (“/”). NMI, no mutation identified; AEDs, antiepileptic drugs; VGB, vigabatrin; VPA, valproic acid; TPM, topiramate; LTG, lamotrigine; LEV, levetiracetam; CLB, clobazam; OXC, oxcarbazepine.
Figure 1(A) Presentation of changes in seizure dynamics in individual patients at baseline (A) and in consecutive semi-annual control points (B, C) on a quadratic scale (1a). Code symbols for individual patients on the vertical axis. On the horizontal axis, the daily seizure frequency is scaled in the range 0–5–10–20–30 seizures per day. The score marks correspond to the seizure frequency for each patient and illustrate the dynamics of treatment efficacy at 6 months and 1 year after rapamycin initiation. (B) rapamycin concentration distributions for the entire population studied in relation to the therapeutic effect obtained in first 6 months (AB) then after another 6 months (BC) that means after 1 year of rapamycin therapy. The achieved therapeutic effect, defined as improvement in seizure control, is compared to the taken dose of the drug per kg with the observed optimum in the range of 3–6.6 mg/kg.
Figure 2Individual therapy regimes (rapamycin dosage) visualized for all patients across the study together with observed seizure intensity. If seizure intensity was described by the caregivers as interval it was visualized as a vertical bar. The vertical axis shows the serum rapamycin concentration ranges, marked for individual patients with red lines. Seizure frequency was quantified by the course of the green lines—decrease with lower frequency and increase with higher frequency of seizures. “Seizure intensity” was defined as seizure number per week.
Figure 3(A,B) Evaluation of the potential influence of additional demographic and pharmacological factors on the efficacy of rapamycin therapy (A) and evaluation of the optimal cumulative dose (the total dose of rapamycin taken over the course of treatment) of rapamycin in the studied group of patients (B) resulting from mathematical modeling. Only variables related to rapamycin dosage were consistently and clearly associated with the model performance. Detected feature importance from autoML model (A) and partial-dependence plots (B) showed non-linear relationship between therapeutic concentration of rapamycin and total cumulative dose of the drug taken during given period of therapy. The ratio of seizure reduction within these dependencies, expressed as average prediction. We used the DALEX package (30) to find relationship between individual variables in ML model and predicted outcome. First, we used it to calculate feature importance. In plot 3 (“feature importance”), one can see average drop in classifier accuracy when variable was permuted (scrambled). Higher loss meant higher importance. Then, we calculated partial dependence plots for two most important variables (rapamycin dosage on previous visit and cumulative rapamycin dosage), plots on the right hand side showed the average prediction of ML model given different levels of those variables.
Adverse events of any cause reported during the rapamycin treatment.
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| Any adverse event | 21 | 0 | 7 | 0 | 28 | 0 |
| Stomatitis | 8 | 0 | 3 | 0 | 11 | 0 |
| Upper respiratory tract infection | 12 | 0 | 3 | 0 | 15 | |
| Diarrhea | 0 | 0 | 0 | 0 | 0 | 0 |
| Nasopharyngitis | 0 | 0 | 0 | 0 | 0 | 0 |
| Rash | 0 | 0 | 0 | 0 | 0 | 0 |
| Vomiting | 0 | 0 | 0 | 0 | 0 | 0 |
| Headache | 0 | 0 | 0 | 0 | 0 | 0 |
| Hypercholesterolemia, hypertrigliceridemia | 0 | 0 | 1 | 0 | 1 | 0 |
| Decreased appetite | 1 | 0 | 0 | 0 | 1 | 0 |
| Acne | 0 | 0 | 0 | 0 | 0 | 0 |
| Pharyngitis | 0 | 0 | 0 | 0 | 0 | 0 |
| Fatigue | 0 | 0 | 0 | 0 | 0 | 0 |
| Pneumonia | 0 | 0 | 0 | 0 | 0 | 0 |