About

Dr. Peter J. West is a faculty member associated with the Anticonvulsant Drug Development (ADD) Program at the University of Utah College of Pharmacy. His research involves projects related to epilepsy and seizure disorders, including work on the Controlled Cortical Impact Post Traumatic Epilepsy slice electrophysiology project. Dr. West mentors undergraduate researchers who have received University Research Opportunity Program (UROP) awards to support their work on epilepsy-related studies. His lab contributes to the longstanding ADD Program, which has been a national model for epilepsy research since its founding in 1975 and is the longest-running NIH-funded program of its kind. The program focuses on discovery and development of anticonvulsant drugs to improve the lives of people living with epilepsy and seizure disorders.

Research topics

• Medicine
• Internal medicine
• Pharmacology
• Psychiatry
• Anesthesia
• Neuroscience
• Pediatrics
• Psychology

Selected publications

• Targeted Reduction of Excessive Mitochondrial Superoxide by Mitoquinone Rescues Cognitive Impairment Without Affecting Spontaneous Recurrent Seizures in a Mouse Model of Temporal Lobe Epilepsy

  Antioxidants · 2026-02-18

  articleOpen access

  Cognitive impairment is a major comorbidity in temporal lobe epilepsy (TLE), yet its underlying pathophysiology remains poorly understood and current therapies provide minimal benefit. While oxidative stress has traditionally been viewed as a precursor to cell death-mediated cognitive decline, cell death is absent in many patients and preclinical models with memory impairment. Here, we tested whether excessive mitochondrial reactive oxygen species (ROS) actively contribute to memory impairment through mechanisms distinct from cell death. Using Kv1.1 knockout (KO) mice, a TLE model with mitochondrial respiratory chain complex I (MRCI) impairment, we found elevated hippocampal mitochondrial superoxide, impaired recognition memory, deficits in synaptic plasticity, and abnormal sharp wave-ripple oscillations. Applying the MRCI inhibitor rotenone to wild-type hippocampal slices caused increased superoxide and mirrored electrophysiology deficits. Both acute and sub-chronic treatment with the mitochondria-targeted antioxidant mitoquinone (MitoQ) reduced superoxide levels, rescued synaptic plasticity, restored network activity, and normalized memory performance in KO mice-without altering seizure frequency, severity, or neuronal excitability. Our results identify mitochondrial superoxide as a reversible driver of hippocampal dysfunction in epilepsy and demonstrate that mitochondria-targeted antioxidant therapy can restore cognition despite persistent seizures. This study provides proof-of-concept for novel treatments improving cognitive comorbidities in TLE beyond seizure control.

  PublisherOA PDFDOI

• Epilepsy Therapy Screening From 1975 to 2026 and Beyond: Merging Established and New Approaches to Develop Novel Therapies

  Epiliepsy currents/Epilepsy currents · 2026-04-20

  articleOpen access

  Antiepileptic Drug Development: II. Anticonvulsant Drug Screening Krall RL, Penry JK, White BG, Kupferberg HJ, Swinyard EA. Epilepsia . 1978 Aug;19(4):409-28. doi:10.1111/j.1528-1157.1978.tb04507.x. PMID: 699894 By means of the maximal electroshock seizure test, the subcutaneous pentylenetetrazol seizure threshold test, and the Rotorod minimal neurotoxicity test, the Anticonvulsant Screening Project has evaluated the activity of 1,495 experimental compounds accessioned in the first 2 years. A three-screen protocol for appraising these compounds has proved reliable, fast, and inexpensive. Preliminary data show that 430 of these compounds have good anticonvulsant activity. Completed evaluations of 352 identified 16 that have anticonvulsant activity at doses less than 75 mg/kg and protective indices greater than 5.0.

  PublisherDOI

• DynamoSort: Using machine learning approaches for the automatic classification of seizure dynamotypes

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-17

  preprintOpen access

  Abstract Objective Epilepsy is characterised by unprovoked and recurring seizures, which can be electrically measured using electroencephalograms (EEG). To better understand the underlying mechanisms of seizures, researchers are exploring their temporal dynamics through the lens of dynamical systems modelling. Seizure initiation and termination patterns of spiking amplitude and frequency can be sorted into “dynamotypes”, which may be able to serve as biomarkers for intervention. However, manual classification of these dynamotypes requires trained raters and is prone to variability. To address this, we developed DynamoSort, a machine-learning algorithm for automatic seizure onset and offset classification. Methods We used approximately 2100 seizures from an intra-amygdala kainic acid (IAK) mouse model of mesial temporal lobe epilepsy, categorized by five trained raters. MATLAB’s classification learner application was used to create an ensemble model to score and label dynamotypes of individual seizures based on spiking and frequency features. Results Dynamotype classification of real EEG data lacks a definitive ground truth, with mean inter-rater agreement at 73.4% for onset and 64.2% for offset types. Despite this, DynamoSort achieved a mean area under the curve (AUC) of 0.81 for onset and a mean AUC of 0.75 for offset types. Machine-human agreement was not significantly different from human-to-human agreement. To address the lack of ground truth in ratings, DynamoSort assigns probabilistic scores (-20 to 20), to indicate similarity to each seizure dynamotype based on spiking features, allowing for a characterization of seizure dynamics on a spectrum rather than the traditional qualitative taxonomy. Significance Automating the classification of dynamotypes is a critical step for their inclusion as a biomarker in clinical and research applications. DynamoSort is a straightforward, open-access tool that uses automatic labelling and probabilistic scoring to quantify subtle changes in seizure onset and offset dynamics. Key Points Dynamotypes are a promising seizure categorization system, but is prone to interrater variability and lacks a ground truth. Machine learning can be used to automatically classify seizure onsets and offsets into appropriate dynamotypes based on spike features. Agreement between DynamoSort and human raters rivals typical agreement rates in trained human raters. DynamoSort uses probabilistic scoring to quantify subtle changes in seizure onset and offset, allowing for a quantitative characterisation.

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• DynamoSort: Using machine learning approaches for the automatic classification of seizure dynamotypes

  Neurocomputing · 2025-07-15

  article
  PublisherDOI

• <scp>Electroencephalographic</scp> signal dimension provides the necessary stability of measurements for the unbiased evaluation of <scp>antiseizure medications</scp> , unlike seizure frequency: Overcoming drawbacks of seizure occurrence variability

  Epilepsia · 2025-10-29

  letterOpen access

  The assessment of antiseizure medication (ASM) efficacy still predominantly relies on changes in seizure frequency (SF). However, in people with epilepsy (PWE), intrinsic variability in seizure timing and patterns significantly influences this assessment, often leading to potential false-negative or false-positive results. A low seizure rate particularly increases false-positive risk, as average seizure-free periods can be statistically comparable to therapy duration. This impacts clinical practice, necessitating extended monitoring and delaying appropriate treatments. For new drug development, PWE with low SFs are typically excluded from clinical trials, hindering patient enrollment and resulting in cohorts that may not adequately represent the general PWE population. To address the well-known limitations of SF as a therapeutic marker, in our recent study published in Epilepsia (doi: https://doi.org/10.1111/epi.18397), we utilized a widely used mouse model of acquired epilepsy that exhibits similar SF variability to human patients. Using recurrence quantification analysis (RQA), a robust method for analyzing short, noisy, and nonstationary time series like the electroencephalogram (EEG), we demonstrated that electroencephalographic signal dimension serves as a reliable, SF-independent measure of both seizure susceptibility and therapeutic response to ASMs. The electroencephalographic signal dimension (DIM), computed from RQA, is an inverse index of the average degree of correlation among brain cells. As we demonstrated in our paper (doi: https://doi.org/10.1111/epi.18397), DIM shows peculiar features to overcome the abovementioned limitations. DIM leverages the network disease characteristic of epilepsy, which arises from excessive correlation among firing neurons, thus acting as an (inverse) proxy of epileptic tissue internal correlation directly underpinning seizure susceptibility and modulating the EEG activity. In our study, DIM provided a stable and consistent biomarker for the risk of developing a seizure (i.e., seizure susceptibility), regardless of whether a seizure actually occurs. This makes DIM a superior measure to SF, which is affected by the inherent stochastic nature of seizure occurrence, which is modulated by the system's susceptibility but is not a direct and continuous measure of it. This makes SF often inconsistent, especially in subjects with low seizure rates. These findings highlighted that DIM offers immediate benefit in preclinical studies and may hold promise for clinical application. We now present new, compelling evidence that further strengthens and extends our initial findings, consolidating the relevance and reliability of electroencephalographic signal dimension as a biomarker for seizure susceptibility compared to seizure rate. We used the same mice and methodology as published, basing our reasoning on the fact that no evidence supports any effect on seizure susceptibility of drug vehicles (NaCl .9% or methylcellulose .5%, administered intraperitoneally). Therefore, for each mouse, both SF and DIM measured during baseline should be statistically equivalent to those measured during vehicle administration. This implies both metrics must satisfy two related statistical relationships: (1) baseline and vehicle measurements should be significantly correlated; and (2) they can be modeled by a regression line with a slope statistically equal to 1, with minor fluctuations around unity. Accordingly, we first established the existence of statistically significant correlations between baseline and vehicle measurements. The DIM measurements were always significantly correlated across all groups of mice, whereas only a minority of SF values showed significant correlation, revealing inconsistencies among mice treated with the same vehicle (Figure 1, top left table). Such differences in correlation statistics are rooted in the respective frequency distributions. Whereas DIM showed a Gaussian-like distribution consistent with a sensible measure of individual system's susceptibility, SF gave rise to exponential frequency rate distribution reminiscent of a Poisson/rare-event-like stochastic phenomenon (Figure 1, top right histograms), due to the inherent stochastic nature of seizure occurrence. To confirm the ability of both DIM and SF to meet the baseline–vehicle equivalence constraints, we adopted an ordinary linear regression approach to evaluate how close slope values were to unity, in the presence of possible systematic biases (e.g., circadian rhythms, manipulation or environmental stress, equipment noise; Figure 1A–F). We found that the slopes of DIM regression lines were all statistically significant and distributed, on average, around unity (Figure 1A–E; mean ± SEM = .92 ± .19). In contrast, SF regression statistics were poor (Figure 1A–F, right graphs) and even inconsistent with their respective correlation analysis, as in the case of the phenytoin–vehicle group, which showed no significant regression line despite a significant correlation. Finally, to exclude that the slopes close to unity in the DIM regressions were spurious, because they were induced by the systematic biases rather than driven by the actual equivalence between baseline and vehicle measurements, we ran a second regression analysis setting the intercepts to zero. The closeness of DIM slope values to unity was consistently confirmed and strengthened for each group of mice (Figure 2A–F, left graphs). In contrast, SF measurements continued to show poor regression statistics, confirming that their intrinsic high variability prevents a stable relationship among measurements (Figure 2A–F, right graphs). The sequence of our analyses provides clear biologically and statistically sound evidence of DIM robustness and stability compared to SF. Such a stable relationship between baseline and vehicle is a necessary requirement for an unbiased evaluation of any ASM efficacy. This critical requirement is poorly satisfied by SF but is perfectly met by DIM, highlighting the importance of shifting the focus from seizure rate to the susceptibility to undergo a seizure. Therefore, our findings further confirm the limitations of SF as a biomarker of seizure susceptibility and support DIM as a valid and robust alternative to SF in preclinical testing of experimental therapeutics with translational potential in clinical settings. If validated for human use, DIM's ability to measure seizure risk could pave the way for a paradigm shift from continuous to on-demand or intermittent therapy to rapidly decrease the risk of a seizure. This targeted approach could also reduce the side effects associated with chronic medication. The successful implementation of DIM's use will rely upon the development of an algorithm for real-time DIM calculation, integrated into advanced electroencephalographic closed loop systems. The authors wish to thank Kyle Thomson for his helpful technical support. The work presented has been funded in whole or in part with federal funds from the National Institute of Neurological Disorders and Stroke, National Institutes of Health, Department of Health and Human Services, under contract No. HHS 75N95022C00007. We acknowledge the CINECA award under the ISCRA initiative for the availability of high-performance computing resources and support. Open access funding provided by BIBLIOSAN. None of the authors has any conflict of interest to disclose. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. The reference data are available in Rizzi M, West PJ, Vezzani A, Wilcox KS, Giuliani A. EEG signal dimension is an index of seizure propensity and antiseizure medication effects in a mouse model of acquired epilepsy. Epilepsia. 2025;66:3035–3047. https://doi.org/10.1111/epi.18397.

  PublisherOA PDFDOI

• Discovery of novel hybrid pyrrolidin-2-one derivatives exhibiting potent antiseizure and antinociceptive effects in preclinical models

  Biomedicine & Pharmacotherapy · 2025-11-01

  articleOpen access

  In the present study, a series of novel derivatives based on the pyrrolidin-2-one scaffold were designed using a framework combination approach, synthesized, and comprehensively evaluated through in vitro and in vivo assays conducted on Swiss albino male mice. The obtained hybrid molecules demonstrated potent and broad-spectrum antiseizure activity in key preclinical seizure models. Following intraperitoneal (i.p.) administration, compound (R)-9, identified as the lead molecule, provided robust protection across all tested seizure paradigms, with ED₅₀ values of 64.3 mg/kg (maximal electroshock test), 26.3 mg/kg (6 Hz, 32 mA), and 37.8 mg/kg (6 Hz, 44 mA). In addition, (R)-9 was active in the pentylenetetrazole (PTZ)-induced kindling model as well as in spontaneous electrographic bursting, an in vitro model of pharmacoresistant seizure-like activity at a concentration of 120 μM. Moreover, it significantly increased seizure threshold in the ivPTZ test. Importantly, (R)-9 also exhibited strong antinociceptive properties. No adverse effects on motor coordination and grip strength were observed at effective doses. Pharmacokinetic profiling and in vitro ADME-Tox evaluation showed satisfying drug-like characteristics of (R)-9, including metabolic stability in human liver microsomes, interaction with cytochrome P450 enzymes and hepatotoxicity (HepG2 cell line). In vitro binding and functional assays suggest a multimodal mechanism of action. Besides its TRPV1 antagonistic activity, (R)-9 effectively inhibited voltage-gated sodium channels at a concentration of 50 μM in electrophysiological studies. Collectively, these findings support the further preclinical development of (R)-9 as a promising candidate for the treatment of epilepsy and pain-related disorders.

  PublisherDOI

• <scp>EEG</scp> signal dimension is an index of seizure propensity and antiseizure medication effects in a mouse model of acquired epilepsy

  Epilepsia · 2025-04-09

  articleOpen access

  OBJECTIVE: Variability in the frequency, timing, and pattern of seizures may influence the assessment of the effect of antiseizure medications (ASMs) when measuring seizure frequency, especially in patients with infrequent seizures. A low seizure rate is an exclusion criterion for enrollment of patients with epilepsy in clinical trials and requires prolonged periods of seizure monitoring, thus delaying appropriate treatment interventions. We investigated whether an electroencephalogram (EEG)-based complexity measure of seizure susceptibility of epileptic mice provides a reliable alternative to seizure frequency for evaluating the efficacy of ASMs. METHODS: We used a mouse model of acquired epilepsy characterized by variability in seizure frequency over time and among mice, as observed in humans. We analyzed EEG recordings from chronic epileptic mice (n = 106) at baseline and during treatment with phenobarbital, valproate, carbamazepine, or phenytoin. We used recurrence quantification analysis to detect increased autocorrelation and critical slowing-down, two signatures of criticality that together contribute to estimate the dimension of phase-space of the EEG signals. The measurements of dimension and seizure frequency were compared as proxies for seizure susceptibility by correlation tests and evaluation of ASM efficacy. RESULTS: Dimension provided a statistically robust (inverse) estimate of seizure susceptibility of mice, including mice with low seizure frequency or no seizures during the observation periods. In contrast, seizure frequency provided a reliable measure only in mice with a high seizure rate. Consistently, evaluation of ASM efficacy using dimension variations accurately reproduced seizure responsiveness patterns in this mouse model. SIGNIFICANCE: EEG-based dimension provides a reliable measure of mouse propensity to experience seizures as well as ASM efficacy, regardless of seizure rates. Measuring dimension variation should facilitate the inclusion of subjects with low seizure rate in preclinical and clinical trials while also shortening the periods of monitoring. This could accelerate both the development of new treatments and therapeutic decisions in the medical field.

  PublisherOA PDFDOI

• ASSESSMENT OF CELL-TYPE-SPECIFIC EXCITATORY SYNAPTIC STRENGTH IN THE DORSOLATERAL STRIATUM OF GOAL-DIRECTED AND HABITUAL COCAINE-SEEKING BEHAVIOR

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-10-08

  preprintOpen access

  Abstract With repeated exposure to addictive drugs, there is a shift from drug abuse to drug addiction that is mediated by the transition from goal-directed to habitual control. It is well known that the development of habitual control over behavior relies upon cell-type-specific synaptic changes in both D1 and D2 medium spiny neurons (MSNs) in dorsal striatum. Specifically, habitual behavior is mediated by increased synaptic strength in D2 MSNs in dorsolateral striatum (DLS), suggesting similar cell-type-specific synaptic changes may underlie the development of habitual cocaine-seeking behavior. However, cell-type-specific synaptic changes have not been evaluated in DLS in this context. Therefore, we trained rats to self-administer cocaine in a cocaine self-administration paradigm that allows for differentiation of goal-directed vs. habitual cocaine-seeking behavior. Moreover, we used a viral vector under a D2-specific promoter to fluorescently label D2 MSNs with eYFP in DLS. Evoked excitatory postsynaptic currents (EPSCs) were used to determine AMPA:NMDA receptor ratio and the rectification index. Surprisingly, we did not observe any significant differences in these measures in DLS of cocaine-seeking rats, regardless of whether cocaine seeking was under habitual control. Interestingly, preliminary observations revealed significant changes in the paired pulse ratio (PPR), suggesting that presynaptic mechanisms may be involved in the development of habitual control over cocaine seeking. Overall, however, these results suggest there are no changes in postsynaptic strength of D2 MSNs in the DLS of rats with an extended history of cocaine self-administration and regardless of whether the cocaine seeking is under goal-directed or habitual control. Significance Statement The study of drug abuse and drug addiction represents a critical area of research with significant public health implications. Importantly, the underlying neurobiology of the transition between drug abuse and drug addiction is not well understood and insights to this transition may aid in the development of novel treatment options. Behaviorally, the shift from goal-directed to habitual control is thought to underly this transition. Much is known about the neurobiology of goal-directed and habitual behavior, however the transition in the context of drug-seeking is not well defined. We observed no significant differences in measures of synaptic strength, suggesting such postsynaptic neuroplasticity in the dorsolateral striatum is not involved in this transition.

  PublisherOA PDFDOI

• Discovery and Profiling of Novel Multi-Mechanistic Phenylglycinamide Derivatives as Potent Antiseizure and Antinociceptive Drug Candidates

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access
  PublisherDOI

• Discovery and Profiling of New Multimodal Phenylglycinamide Derivatives as Potent Antiseizure and Antinociceptive Drug Candidates

  ACS Chemical Neuroscience · 2024-08-21 · 3 citations

  articleOpen access

  We developed a focused series of original phenyl-glycinamide derivatives which showed potent activity across in vivo mouse seizure models, namely, maximal electroshock (MES) and 6 Hz (using both 32 and 44 mA current intensities) seizure models. Following intraperitoneal (i.p.) administration, compound (R)-32, which was identified as a lead molecule, demonstrated potent protection against all seizure models with ED50 values of 73.9 mg/kg (MES test), 18.8 mg/kg (6 Hz, 32 mA test), and 26.5 mg/kg (6 Hz, 44 mA test). Furthermore, (R)-32 demonstrated efficacy in both the PTZ-induced kindling paradigm and the ivPTZ seizure threshold test. The expression of neurotrophic factors, such as mature brain-derived neurotrophic factor (mBDNF) and nerve growth factor (NGF), in the hippocampus and/or cortex of mice, and the levels of glutamate and GABA were normalized after PTZ-induced kindling by (R)-32. Importantly, besides antiseizure activity, (R)-32 demonstrated potent antinociceptive efficacy in formalin-induced pain, capsaicin-induced pain, as well as oxaliplatin- and streptozotocin-induced peripheral neuropathy in mice (i.p.). No influence on muscular strength and body temperature in mice was observed. Pharmacokinetic studies and in vitro ADME-Tox data (i.e., high metabolic stability in human liver microsomes, a weak influence on CYPs, no hepatotoxicity, satisfactory passive transport, etc.) proved favorable drug-like properties of (R)-32. Thermal stability of (R)-32 shown in thermogravimetry and differential scanning calorimetry gives the opportunity to develop innovative oral solid dosage forms loaded with this compound. The in vitro binding and functional assays indicated its multimodal mechanism of action. (R)-32, beyond TRPV1 antagonism, inhibited calcium and sodium currents at a concentration of 10 μM. Therefore, the data obtained in the current studies justify a more detailed preclinical development of (R)-32 for epilepsy and pain indications.

  PublisherDOI

Frequent coauthors

• Karen S. Wilcox

  University of Utah

  23 shared

• Erica Webster

  Université de Montréal

  22 shared

• J. Helen Cross

  Epilepsy Research UK

  22 shared

• Clifford B. Saper

  Hadassah Medical Center

  22 shared

• Lisa Soeby

  Université de Montréal

  22 shared

• William D. Gaillard

  George Washington University Hospital

  22 shared

• Arthur Cukiert

  22 shared

• Andreas Schulze‐Bonhage

  Stichting Epilepsie Instellingen Nederland

  22 shared

Labs

• ADD LabPI

  The ADD Program Students page at the University of Utah showcases students involved in addiction, delirium, and CNS disorders research, highlighting their academic pursuits and contributions to the field.