Combination of ciprofloxacin/celecoxib as a novel therapeutic strategy for ALS.

OBJECTIVE: This study aimed to evaluate the safety and tolerability of a fixed-dose co-formulation of ciprofloxacin and celecoxib (PrimeC) in patients with amyotrophic lateral sclerosis (ALS), and to examine its effects on disease progression and ALS-related biomarkers. METHODS: In this proof of concept, open-label, phase IIa study of PrimeC in 15 patients with ALS, participants were administered PrimeC thrice daily for 12 months. The primary endpoints were safety and tolerability. Exploratory endpoints included disease progression outcomes such as forced vital capacity, revised ALS functional rating scale, and effect on algorithm-predicted survival. In addition, indications of a biological effect were assessed by selected biomarker analyses, including TDP-43 and LC3 levels in neuron-derived exosomes (NDEs), and serum neurofilaments. RESULTS: Four participants experienced adverse events (AEs) related to the study drug. None of these AEs were unexpected, and most were mild or moderate (69%). Additionally, no serious AEs were related to the study drug. One participant tested positive for COVID-19 and recovered without complications, and no other abnormal laboratory investigations were found. Participants' survival compared to their predictions showed no safety concerns. Biomarker analyses demonstrated significant changes associated with PrimeC in neural-derived exosomal TDP-43 levels and levels of LC3, a key autophagy marker. INTERPRETATION: This study supports the safety and tolerability of PrimeC in ALS. Biomarker analyses suggest early evidence of a biological effect. A placebo-controlled trial is required to disentangle the biomarker results from natural progression and to evaluate the efficacy of PrimeC for the treatment of ALS. Summary for social media if publishedTwitter handles: @NeurosenseT, @ShiranZimri•What is the current knowledge on the topic? ALS is a severe neurodegenerative disease, causing death within 2-5 years from diagnosis. To date there is no effective treatment to halt or significantly delay disease progression.•What question did this study address? This study assessed the safety, tolerability and exploratory efficacy of PrimeC, a fixed dose co-formulation of ciprofloxacin and celecoxib in the ALS population.•What does this study add to our knowledge? This study supports the safety and tolerability of PrimeC in ALS, and exploratory biomarker analyses suggest early insight for disease related-alteration.•How might this potentially impact the practice of neurology? These results set the stage for a larger, placebo-controlled study to examine the efficacy of PrimeC, with the potential to become a new drug candidate for ALS.

Evidence for generalizability of edaravone efficacy using a novel machine learning risk-based subgroup analysis tool.

Introduction: The edaravone development program for amyotrophic lateral sclerosis (ALS) included trials MCI186-16 (Study 16) and MCI186-19 (Study 19). A cohort enrichment strategy was based on a Study 16 post hoc analysis and applied to Study 19 to elucidate a treatment effect in that study. To determine whether the Study 19 results could be generalized to a broader ALS population, we used a machine learning (ML) model to create a novel risk-based subgroup analysis tool. Methods: A validated ML model was used to rank order all Study 16 participants by predicted time to 50% expected vital capacity. Subjects were stratified into nearest-neighbor risk-based subgroups that were systematically expanded to include the entire Study 16 population. For each subgroup, a statistical analysis generated heat maps that revealed statistically significant effect sizes. Results: A broad region of the Study 16 heat map with significant effect sizes was identified, including up to 70% of the trial population. Incorporating participants identified in the cohort enrichment strategy yielded a broad group comprising 76% of the original participants with a statistically significant treatment effect. This broad group spanned the full range of the functional score progression observed in Study 16. Conclusions: This analysis, applying predictions derived using an ML model to a novel methodology for subgroup identification, ascertained a statistically significant edaravone treatment effect in a cohort of participants with broader disease characteristics than the Study 19 inclusion criteria. This novel methodology may assist clinical interpretation of study results and potentially inform efficient future clinical trial design strategies.

Development and validation of a machine-learning ALS survival model lacking vital capacity (VC-Free) for use in clinical trials during the COVID-19 pandemic.

Introduction: Vital capacity (VC) is routinely used for ALS clinical trial eligibility determinations, often to exclude patients unlikely to survive trial duration. However, spirometry has been limited by the COVID-19 pandemic. We developed a machine-learning survival model without the use of baseline VC and asked whether it could stratify clinical trial participants and a wider ALS clinic population. Methods. A gradient boosting machine survival model lacking baseline VC (VC-Free) was trained using the PRO-ACT ALS database and compared to a multivariable model that included VC (VCI) and a univariable baseline %VC model (UNI). Discrimination, calibration-in-the-large and calibration slope were quantified. Models were validated using 10-fold internal cross validation, the VITALITY-ALS clinical trial placebo arm and data from the Emory University tertiary care clinic. Simulations were performed using each model to estimate survival of patients predicted to have a > 50% one year survival probability. Results. The VC-Free model suffered a minor performance decline compared to the VCI model yet retained strong discrimination for stratifying ALS patients. Both models outperformed the UNI model. The proportion of excluded vs. included patients who died through one year was on average 27% vs. 6% (VCI), 31% vs. 7% (VC-Free), and 13% vs. 10% (UNI). Conclusions. The VC-Free model offers an alternative to the use of VC for eligibility determinations during the COVID-19 pandemic. The observation that the VC-Free model outperforms the use of VC in a broad ALS patient population suggests the use of prognostic strata in future, post-pandemic ALS clinical trial eligibility screening determinations.

Design and analysis of a clinical trial using previous trials as historical control.

BACKGROUND/AIMS: For single arm trials, a treatment is evaluated by comparing an outcome estimate to historically reported outcome estimates. Such a historically controlled trial is often analyzed as if the estimates from previous trials were known without variation and there is no trial-to-trial variation in their estimands. We develop a test of treatment efficacy and sample size calculation for historically controlled trials that considers these sources of variation. METHODS: We fit a Bayesian hierarchical model, providing a sample from the posterior predictive distribution of the outcome estimand of a new trial, which, along with the standard error of the estimate, can be used to calculate the probability that the estimate exceeds a threshold. We then calculate criteria for statistical significance as a function of the standard error of the new trial and calculate sample size as a function of difference to be detected. We apply these methods to clinical trials for amyotrophic lateral sclerosis using data from the placebo groups of 16 trials. RESULTS: We find that when attempting to detect the small to moderate effect sizes usually assumed in amyotrophic lateral sclerosis clinical trials, historically controlled trials would require a greater total number of patients than concurrently controlled trials, and only when an effect size is extraordinarily large is a historically controlled trial a reasonable alternative. We also show that utilizing patient level data for the prognostic covariates can reduce the sample size required for a historically controlled trial. CONCLUSION: This article quantifies when historically controlled trials would not provide any sample size advantage, despite dispensing with a control group.

Pilot trial of inosine to elevate urate levels in amyotrophic lateral sclerosis.

OBJECTIVE: To test the safety, tolerability, and urate-elevating capability of the urate precursor inosine taken orally or by feeding tube in people with amyotrophic lateral sclerosis (ALS). METHODS: This was a pilot, open-label trial in 25 participants with ALS. Treatment duration was 12 weeks. The dose of inosine was titrated at pre-specified time points to elevate serum urate levels to 7-8 mg/dL. Primary outcomes were safety (as assessed by the occurrence of adverse events [AEs]) and tolerability (defined as the ability to complete the 12-week study on study drug). Secondary outcomes included biomarkers of oxidative stress and damage. As an exploratory analysis, observed outcomes were compared with a virtual control arm built using prediction algorithms to estimate ALSFRS-R scores. RESULTS: Twenty-four out of 25 participants (96%) completed 12 weeks of study drug treatment. One participant was unable to comply with study visits and was lost to follow-up. Serum urate rose to target levels in 6 weeks. No serious AEs attributed to study drug and no AEs of special concern, such as urolithiasis and gout, occurred. Selected biomarkers of oxidative stress and damage had significant changes during the study period. Observed changes in ALSFRS-R did not differ from baseline predictions. INTERPRETATION: Inosine appeared safe, well tolerated, and effective in raising serum urate levels in people with ALS. These findings, together with epidemiological observations and preclinical data supporting a neuroprotective role of urate in ALS models, provide the rationale for larger clinical trials testing inosine as a potential disease-modifying therapy for ALS.

Improved stratification of ALS clinical trials using predicted survival.

INTRODUCTION: In small trials, randomization can fail, leading to differences in patient characteristics across treatment arms, a risk that can be reduced by stratifying using key confounders. In ALS trials, riluzole use (RU) and bulbar onset (BO) have been used for stratification. We hypothesized that randomization could be improved by using a multifactorial prognostic score of predicted survival as a single stratifier. METHODS: We defined a randomization failure as a significant difference between treatment arms on a characteristic. We compared randomization failure rates when stratifying for RU and BO ("traditional stratification") to failure rates when stratifying for predicted survival using a predictive algorithm. We simulated virtual trials using the PRO-ACT database without application of a treatment effect to assess balance between cohorts. We performed 100 randomizations using each stratification method - traditional and algorithmic. We applied these stratification schemes to a randomization simulation with a treatment effect using survival as the endpoint and evaluated sample size and power. RESULTS: Stratification by predicted survival met with fewer failures than traditional stratification. Stratifying predicted survival into tertiles performed best. Stratification by predicted survival was validated with an external dataset, the placebo arm from the BENEFIT-ALS trial. Importantly, we demonstrated a substantial decrease in sample size required to reach statistical power. CONCLUSIONS: Stratifying randomization based on predicted survival using a machine learning algorithm is more likely to maintain balance between trial arms than traditional stratification methods. The methodology described here can translate to smaller, more efficient clinical trials for numerous neurological diseases.

Longitudinal modeling to predict vital capacity in amyotrophic lateral sclerosis.

OBJECTIVES: Death in amyotrophic lateral sclerosis (ALS) patients is related to respiratory failure, which is assessed in clinical settings by measuring vital capacity. We developed ALS-VC, a modeling tool for longitudinal prediction of vital capacity in ALS patients. METHODS: A gradient boosting machine (GBM) model was trained using the PRO-ACT (Pooled Resource Open-access ALS Clinical Trials) database of over 10,000 ALS patient records. We hypothesized that a reliable vital capacity predictive model could be developed using PRO-ACT. RESULTS: The model was used to compare FVC predictions with a 30-day run-in period to predictions made from just baseline. The internal root mean square deviations (RMSD) of the run-in and baseline models were 0.534 and 0.539, respectively, across the 7L FVC range captured in PRO-ACT. The RMSDs of the run-in and baseline models using an unrelated, contemporary external validation dataset (0.553 and 0.538, respectively) were comparable to the internal validation. The model was shown to have similar accuracy for predicting SVC (RMSD = 0.562). The most important features for both run-in and baseline models were "Baseline forced vital capacity" and "Days since baseline." CONCLUSIONS: We developed ALS-VC, a GBM model trained with the PRO-ACT ALS dataset that provides vital capacity predictions generalizable to external datasets. The ALS-VC model could be helpful in advising and counseling patients, and, in clinical trials, it could be used to generate virtual control arms against which observed outcomes could be compared, or used to stratify patients into slowly, average, and rapidly progressing subgroups.

Predicting disease progression in amyotrophic lateral sclerosis.

OBJECTIVE: It is essential to develop predictive algorithms for Amyotrophic Lateral Sclerosis (ALS) disease progression to allow for efficient clinical trials and patient care. The best existing predictive models rely on several months of baseline data and have only been validated in clinical trial research datasets. We asked whether a model developed using clinical research patient data could be applied to the broader ALS population typically seen at a tertiary care ALS clinic. METHODS: Based on the PRO-ACT ALS database, we developed random forest (RF), pre-slope, and generalized linear (GLM) models to test whether accurate, unbiased models could be created using only baseline data. Secondly, we tested whether a model could be validated with a clinical patient dataset to demonstrate broader applicability. RESULTS: We found that a random forest model using only baseline data could accurately predict disease progression for a clinical trial research dataset as well as a population of patients being treated at a tertiary care clinic. The RF Model outperformed a pre-slope model and was similar to a GLM model in terms of root mean square deviation at early time points. At later time points, the RF Model was far superior to either model. Finally, we found that only the RF Model was unbiased and was less subject to overfitting than either of the other two models when applied to a clinic population. INTERPRETATION: We conclude that the RF Model delivers superior predictions of ALS disease progression.

Being PRO-ACTive: What can a Clinical Trial Database Reveal About ALS?

Advancing research and clinical care, and conducting successful and cost-effective clinical trials requires characterizing a given patient population. To gather a sufficiently large cohort of patients in rare diseases such as amyotrophic lateral sclerosis (ALS), we developed the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) platform. The PRO-ACT database currently consists of >8600 ALS patient records from 17 completed clinical trials, and more trials are being incorporated. The database was launched in an open-access mode in December 2012; since then, >400 researchers from >40 countries have requested the data. This review gives an overview on the research enabled by this resource, through several examples of research already carried out with the goal of improving patient care and understanding the disease. These examples include predicting ALS progression, the simulation of future ALS clinical trials, the verification of previously proposed predictive features, the discovery of novel predictors of ALS progression and survival, the newly identified stratification of patients based on their disease progression profiles, and the development of tools for better clinical trial recruitment and monitoring. Results from these approaches clearly demonstrate the value of large datasets for developing a better understanding of ALS natural history, prognostic factors, patient stratification, and more. The increasing use by the community suggests that further analyses of the PRO-ACT database will continue to reveal more information about this disease that has for so long defied our understanding.

CG0070, a conditionally replicating granulocyte macrophage colony-stimulating factor--armed oncolytic adenovirus for the treatment of bladder cancer.

PURPOSE: The purpose of this study was to examine the tumor specificity, cytotoxicity, and granulocyte macrophage colony-stimulating factor expression of CG0070, a conditionally replicating oncolytic adenovirus, in human bladder transitional cell carcinoma (TCC) cell lines and determine its antitumor efficacy in bladder TCC tumor models. EXPERIMENTAL DESIGN: Virus yield and cytotoxicity assays were used to determine tumor specificity and virus replication-mediated cytotoxicity of CG0070 in a panel of human bladder TCC cell lines and primary cells in vitro. Two s.c. and one orthotopic bladder TCC xenograft tumor models were used to assess antitumor activity of CG0070. RESULTS: In a matched isogenic pair of cell lines with differing retinoblastoma (Rb) pathway status, CG0070 showed selective E1a and granulocyte macrophage colony-stimulating factor (GM-CSF) expression in Rb pathway-defective cells. CG0070 replicated in Rb-defective bladder TCC cell lines as efficiently as wild-type adenovirus but produced 100-fold less virus in normal human cells. CG0070 was up to 1,000-fold more cytotoxic in Rb pathway-defective bladder TCC cells in comparison with normal human cells. Antitumor activity of CG0070 was shown in two bladder TCC s.c. xenograft tumor models following intratumoral injections and intravesical treatment in an orthotopic xenograft tumor model when compared with PBS treatment. CONCLUSIONS: In vitro and in vivo studies showed the selective replication, cytotoxicity, GM-CSF production, and antitumor efficacy of CG0070 in several bladder TCC models, suggesting a potential utility of this oncolytic agent for the treatment of bladder cancer. Further studies are warranted to show the role of human GM-CSF in the antitumor efficacy of CG0070.

Linked tumor-selective virus replication and transgene expression from E3-containing oncolytic adenoviruses.

Historically, the adenoviral E3 region was found to be nonessential for viral replication in vitro. In addition, adenoviruses whose genome was more than approximately 105% the size of the native genome were inefficiently packaged. These profound observations were used experimentally to insert transgenes into the adenoviral backbone. More recently, however, the reintroduction of the E3 region into oncolytic adenoviruses has been found to positively influence antitumor efficacy in preclinical models and clinical trials. In the studies reported here, the granulocyte-macrophage colony-stimulating factor (GM-CSF) cDNA sequence has been substituted for the E3-gp19 gene in oncolytic adenoviruses that otherwise retained the E3 region. Five viruses that differed slightly in the method of transgene insertion were generated and compared to Ar6pAE2fGmF (E2F/GM/DeltaE3), a previously described E3-deleted oncolytic adenovirus encoding GM-CSF. In all of the viruses, the human E2F-1 promoter regulated E1A expression and GM-CSF expression was under the control of the adenoviral E3 promoter and the packaging signal was relocated immediately upstream from the right terminal repeat. The E3-gp19-deleted viruses had similar cytolytic properties, as measured in vitro by cytotoxicity assays, but differed markedly in their capacity to express and secrete GM-CSF. Ar15pAE2fGmF (E2F/GM/E3b), the virus that produced the highest levels of GM-CSF and retained the native GM-CSF leader sequence, was selected for further analysis. The E2F/GM/E3b and E2F/GM/DeltaE3 viruses exhibited similar cytotoxic activity and GM-CSF production in several tumor cell lines in vitro. However, when compared in vivo in nude mouse xenograft tumor models, E2F/GM/E3b spread through tumors to a greater extent, resulted in higher peak GM-CSF and total exposure levels in both tumor and serum, and was more efficacious than the E3-deleted virus. Using the matched WI-38 (parental) and WI-38-VA13 (simian virus 40 large T antigen transformed) cell pair, GM-CSF was shown to be selectively produced in cells expressing high levels of E2F, indicating that the tumor-selective E2F promoter controlled E1A and GM-CSF expression.

In vitro and in vivo activities of an oncolytic adenoviral vector designed to express GM-CSF.

Oncolytic adenoviruses are being tested as biological cancer therapeutics. Ar6pAE2fF (E2F vector) contains the E2F-1 promoter to regulate the expression of the E1a gene in cells with a disregulated retinoblastoma pathway. Ar6pAE2fmGmF (E2F-GM vector) includes the murine granulocyte-macrophage colony-stimulating factor (GM-CSF) transgene to enhance anti-tumor activity. Both vectors selectively killed human tumor cells in vitro. The E2F-GM vector expressed biologically active murine GM-CSF in vitro and GM-CSF was detected for several days in serum and tumor extracts following injections of established human xenograft tumors. In vivo, both vectors showed significant dose-dependent anti-tumor responses. The E2F-GM vector elicited greater efficacy compared to the E2F vector, demonstrating that GM-CSF enhanced the anti-tumor activity, even in immunodeficient nude mice. Histological analysis showed that both vectors induced necrosis and mononuclear cell infiltration, but only the E2F-GM vector resulted in eosinophil infiltration. Vector replication in vivo was demonstrated. The data showed that intratumoral injection of a GM-CSF-armed oncolytic vector induced potent anti-tumor responses in xenograft tumor models, likely as the result of both oncolytic vector activity and the induction of GM-CSF-mediated inflammation and innate immunity.