Modeling and Dose Schedule Design for Cycle-Specific Chemotherapeutics

Model-based optimal control has been used to synthesize chemotherapy treatment schedules. Constraints on drug delivery or states are used to maintain drug administration within toxicity limits, and the objective function usually minimizes the tumor volume at a prespecified final time. These solutions predict a characteristic 3-phase treatment profile: maximum initial drug delivery; a non-dosing period; and the remainder of the drug delivered at the end of the treatment window. Ethically, however, a doctor cannot allow a tumor to grow untreated, thereby invalidating the controller formulation. Dose schedule development, therefore, requires an alternative formulation to obtain clinically relevant dosing schedules. Dose schedule design for the therapeutic tamoxifen (TM) was investigated using nonlinear model predictive control (NMPC) and a tumor regressionreference trajectory. Performance was dependent on accurate incorporation of the pharmacodynamic (PD) effect, and the desired trajectory was tracked. The techniques evaluated could be adapted to other therapeutics administered over regular intervals, though alterations to the objective function would be necessary forclinical implementation.More detailed cell-level tumor growth models were investigated using population balance equations.Individual cell cycle states were included within the model, as were saturating growth rates representative of Gompertzian growth seen from solid tumors. Open-loop simulations involving two cycle specific therapeutics (S- and M-phase active) questioned the simultaneous adminstration of therapeutics which predicted the largest final tumor volumes. These results require additional investigation, as does the accuracy of the bilinear PD effect structure.A physiologically-based pharmacokinetic (PBPK) model for docetaxel (Doc) disposition in SCID mice was developed based on collected plasma, tumor, and tissue concentration data. This model was scaled to humans and compared against patient Doc plasma data from several clinical trials as well as Doc plasma predictions from other models in the literature. A low-order neutrophil model from the literature was tailored to patient neutrophil samples from the clinical study. The human-scaled PBPK Doc and neutrophil PD models were combined and used to evaluate Doc regimens from the literature. Finally, a nonlinear model predictive controller (NMPC) was synthesized based on the PBPK and PD models and used to develop clinically-relevant dosing regimens under PD constraints

Exposure Matching for Extrapolation of Efficacy in Pediatric Drug Development: Extrapolation of Efficacy in Pediatric Drug Development

During drug development, matching adult systemic exposures of drugs is a common approach for dose selection in pediatric patients when efficacy is partially or fully extrapolated. This is a systematic review of approaches used for matching adult systemic exposures as the basis for dose selection in pediatric trials submitted to the U.S. Food and Drug Administration (FDA) between 1998 and 2012. The trial design of pediatric pharmacokinetic (PK) studies and the pediatric and adult systemic exposure data were obtained from FDA publicly available databases containing reviews of pediatric trials. Exposure matching approaches that were used as the basis for pediatric dose selection were reviewed. The PK data from the adult and pediatric populations were used to quantify exposure agreement between the two patient populations. The main measures were the pediatric PK studies trial design elements and drug systemic exposures (adult and pediatric). There were 31 products (86 trials) with full or partial extrapolation of efficacy with an available PK assessment. Pediatric exposures had a range of mean Cmax and AUC ratios (pediatric/adult) of 0.63-4.19 and 0.36-3.60 respectively. Seven of the 86 trials (8.1%) had a pre-defined acceptance boundary used to match adult exposures. The key PK parameter was consistently predefined for antiviral and anti-infective products. Approaches to match exposure in children and adults varied across products. A consistent approach for systemic exposure matching and evaluating pediatric PK studies is needed to guide future pediatric trials

Preliminary evidence for a lower brain age in children with attention-deficit/hyperactivity disorder.

Attention-deficit hyperactivity disorder (ADHD) is a debilitating disorder with apparent roots in abnormal brain development. Here, we quantified the level of individual brain maturation in children with ADHD using structural neuroimaging and a recently developed machine learning algorithm. More specifically, we compared the BrainAGE index between three groups matched for chronological age (mean ± SD: 11.86 ± 3.25 years): 89 children diagnosed with ADHD, 34 asymptomatic siblings of those children with ADHD, and 21 unrelated healthy control children. Brains of children with ADHD were estimated significantly younger (-0.85 years) than brains of healthy controls (Cohens d = -0.33; p = 0.028, one-tailed), while there were no significant differences between unaffected siblings and healthy controls. In addition, more severe ADHD symptoms were significantly associated with younger appearing brains. Altogether, these results are in line with the proposed delay of individual brain maturation in children with ADHD. However, given the relatively small sample size (N = 144), the findings should be considered preliminary and need to be confirmed in future studies

Mining Exceptional Mediation Models

Lemmerich F, Kiefer C, Langenberg B, Cacho Aboukhalil J, Mayer A. Mining Exceptional Mediation Models. In: Helic D, Leitner G, Stettinger M, Felfernig A, Raś ZW, eds. Foundations of Intelligent Systems. 25th International Symposium, ISMIS 2020, Graz, Austria, September 23–25, 2020, Proceedings. Lecture Notes in Computer Science. Vol 12117. Cham: Springer ; 2020: 318-328

Preliminary evidence for a lower brain age in children with attention-deficit/hyperactivity disorder

Attention-deficit hyperactivity disorder (ADHD) is a debilitating disorder with apparent roots in abnormal brain development. Here, we quantified the level of individual brain maturation in children with ADHD using structural neuroimaging and a recently developed machine learning algorithm. More specifically, we compared the BrainAGE index between three groups matched for chronological age (mean +/- SD: 11.86 +/- 3.25 years): 89 children diagnosed with ADHD, 34 asymptomatic siblings of those children with ADHD, and 21 unrelated healthy control children. Brains of children with ADHD were estimated significantly younger (-0.85 years) than brains of healthy controls (Cohen's d = -0.33; p = 0.028, one-tailed), while there were no significant differences between unaffected siblings and healthy controls. In addition, more severe ADHD symptoms were significantly associated with younger appearing brains. Altogether, these results are in line with the proposed delay of individual brain maturation in children with ADHD. However, given the relatively small sample size (N = 144), the findings should be considered preliminary and need to be confirmed in future studies

Data_Sheet_1_Preliminary evidence for a lower brain age in children with attention-deficit/hyperactivity disorder.PDF

Attention-deficit hyperactivity disorder (ADHD) is a debilitating disorder with apparent roots in abnormal brain development. Here, we quantified the level of individual brain maturation in children with ADHD using structural neuroimaging and a recently developed machine learning algorithm. More specifically, we compared the BrainAGE index between three groups matched for chronological age (mean ± SD: 11.86 ± 3.25 years): 89 children diagnosed with ADHD, 34 asymptomatic siblings of those children with ADHD, and 21 unrelated healthy control children. Brains of children with ADHD were estimated significantly younger (−0.85 years) than brains of healthy controls (Cohen’s d = −0.33; p = 0.028, one-tailed), while there were no significant differences between unaffected siblings and healthy controls. In addition, more severe ADHD symptoms were significantly associated with younger appearing brains. Altogether, these results are in line with the proposed delay of individual brain maturation in children with ADHD. However, given the relatively small sample size (N = 144), the findings should be considered preliminary and need to be confirmed in future studies.</p

A metagenomic analysis for combination therapy of multiple classes of antibiotics on the prevention of the spread of antibiotic-resistant genes

ABSTRACTAntibiotics used systemically to treat infections may have off-target effects on the gut microbiome, potentially resulting in the emergence of drug-resistant bacteria or selection of pathogenic species. These organisms may present a risk to the host and spread to the environment with a risk of transmission in the community. To investigate the risk of emergent antibiotic resistance in the gut microbiome following systemic treatment with antibiotics, this metagenomic analysis project used next-generation sequencing, a custom-built metagenomics pipeline, and differential abundance analysis to study the effect of antibiotics (ampicillin, ciprofloxacin, and fosfomycin) in monotherapy and different combinations at high and low doses, to determine the effect on resistome and taxonomic composition in the gut of Balb/c mice. The results showed that low-dose monotherapy treatments showed little change in microbiome composition but did show an increase in expression of many antibiotic-resistant genes (ARGs) posttreatment. Dual combination treatments allowed the emergence of some conditionally pathogenic bacteria and some increase in the abundance of ARGs despite a general decrease in microbiota diversity. Triple combination treatment was the most successful in inhibiting emergence of relevant opportunistic pathogens and completely suppressed all ARGs after 72 h of treatment. The relative abundances of mobile genetic elements that can enhance transmission of antibiotic resistance either decreased or remained the same for combination therapy while increasing for low-dose monotherapy. Combination therapy prevented the emergence of ARGs and decreased bacterial diversity, while low-dose monotherapy treatment increased ARGs and did not greatly change bacterial diversity