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Abnormal energy metabolism associated with mitochondrial dysfunction is thought to be a major contributor to the progression of neurodegenerative diseases such as Parkinson’s disease (PD). Recent advancements in the field of magnetic resonance (MR) based metabolic imaging provide state-of-the-art technologies for non-invasively probing cerebral energy metabolism under various brain conditions. In this proof-of-principle clinical study, we employed quantitative ³¹P MR spectroscopy (MRS) imaging techniques to determine a constellation of metabolic and bioenergetic parameters, including cerebral adenosine triphosphate (ATP) and other phosphorous metabolite concentrations, intracellular pH and nicotinamide adenine dinucleotide (NAD) redox ratio, and ATP production rates in the occipital lobe of cognitive-normal PD patients, and then we compared them with age-sex matched healthy controls. Small but statistically significant differences in intracellular pH, NAD and ATP contents and ATPase enzyme activity between the two groups were detected, suggesting that subtle defects in energy metabolism and mitochondrial function are quantifiable before regional neurological deficits or pathogenesis begin to occur in these patients. Pilot data aiming to evaluate the bioenergetic effect of mitochondrial-protective bile acid, ursodeoxycholic acid (UDCA) were also obtained. These results collectively demonstrated that in vivo ³¹P MRS-based neuroimaging can non-invasively and quantitatively assess key metabolic-energetic metrics in the human brain. This provides an exciting opportunity to better understand neurodegenerative diseases, their progression and response to treatment.

Sacituzumab govitecan (SG), a Trop‐2‐directed antibody‐drug conjugate, is approved for patients with metastatic triple‐negative breast cancer (mTNBC) who received ≥2 prior systemic therapies (≥1 in metastatic setting). Exposure–response (E‐R) relationships between SG exposure and efficacy and safety outcomes were characterized in 277 patients with mTNBC using data from the phase I/II IMMU‐132‐01 and phase III ASCENT (IMMU‐132‐05) studies. Evaluated endpoints included complete response (CR), objective response rate (ORR), progression‐free survival (PFS), overall survival (OS), and safety endpoints (individual first worst grade of select adverse events (AEs)). E‐R analyses were also conducted for time to first dose reduction or delay. Patients received SG at 8 or 10 mg/kg intravenously on days 1 and 8 of a 21‐day cycle. Average SG‐related serum exposure over the treatment duration (until the event) was consistently the most significant exposure metric correlated with efficacy and safety endpoints. Higher average concentration over the treatment duration for SG (CAVG_SG) was the best predictor of CR and ORR. The model‐predicted proportions of patients with CR and ORR at 10 mg/kg were 4.26% and 32.6%, respectively. Higher CAVG for total antibody was the best predictor of OS and PFS. The model‐predicted probability of OS at 12 months at median lactate dehydrogenase (227 IU/L) was 53%. The probability of grade ≥1 evaluated AEs and the risk of dose reductions and delays significantly increased with increasing CAVG_SG. The model‐predicted proportions of patients with any‐grade AEs were 35.9%, 67.4%, 64.7%, and 67.1% for vomiting, diarrhea, nausea, and neutropenia, respectively (10 mg/kg dose group). Neutropenia was the only evaluated AE for which CAVG_SG was significantly associated with grade ≥3 events. The clinically meaningful efficacy and manageable safety achieved with SG 10 mg/kg on days 1 and 8 of every 21‐day cycle dosing regimen supports the appropriateness of this clinical dosage in patients with mTNBC.

Estimating early exposure of drugs used for the treatment of emergent conditions is challenging because blood sampling to measure concentrations is difficult. The objective of this work was to evaluate predictive performance of two early concentrations and prior pharmacokinetic information for estimating early exposure. The performance of a modeling approach was compared to a non‐compartmental analysis (NCA). A simulation study was performed using literature‐based models for phenytoin (PHT), levetiracetam (LEV) and valproic acid (VPA). These models were used to simulate rich concentration‐time profiles from 0‐2 hours. Profiles without residual unexplained variability (RUV) were used to obtain the true partial area‐under‐the‐curve 0‐2 hours (pAUC). From the profiles with the RUV, two concentrations per patient were randomly selected. These concentrations were analyzed under a population model to obtain individual PopPK pAUCs. The NCA pAUCs were calculated using a linear trapezoidal rule. Percent prediction errors (PPE) for the PopPK pAUCs and NCA pAUCs were calculated. A PPE within ± 20% of the true value was considered a success and the number of successes was obtained for 100 simulated datasets. For PHT, LEV, and VPA, respectively, the median value of the success statistics obtained using the PopPK approach of 81, 92 and 88%were significantly higher than the 72, 80 and 67% using the NCA approach (p<0.05; Mann‐Whitney U test). This study provides a means by which early exposure can be estimated with good precision from two concentrations and a population pharmacokinetics approach. It can be applied to other settings in which early exposures are of interest.