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This chapter contains sections titled: Applications A Brief Review of Linear Regression The Knowledge Gradient for a Linear Model Application to Drug Discovery Application to Dynamic Pricing Bibliographic Notes Problems

Preclinical studies suggest that treatment with neoadjuvant immune checkpoint blockade is associated with enhanced survival and antigen-specific T cell responses compared with adjuvant treatment 1 ; however, optimal regimens have not been defined. Here we report results from a randomized phase 2 study of neoadjuvant nivolumab versus combined ipilimumab with nivolumab in 23 patients with high-risk resectable melanoma ( NCT02519322 ). RECIST overall response rates (ORR), pathologic complete response rates (pCR), treatment-related adverse events (trAEs) and immune correlates of response were assessed. Treatment with combined ipilimumab and nivolumab yielded high response rates (RECIST ORR 73%, pCR 45%) but substantial toxicity (73% grade 3 trAEs), whereas treatment with nivolumab monotherapy yielded modest responses (ORR 25%, pCR 25%) and low toxicity (8% grade 3 trAEs). Immune correlates of response were identified, demonstrating higher lymphoid infiltrates in responders to both therapies and a more clonal and diverse T cell infiltrate in responders to nivolumab monotherapy. These results describe the feasibility of neoadjuvant immune checkpoint blockade in melanoma and emphasize the need for additional studies to optimize treatment regimens and to validate putative biomarkers. Neoadjuvant combination treatment with nivolumab and ipilimumab in patients with high-risk melanoma results in higher response rates than nivolumab monotherapy and warrants future optimization of dosing regimens to preserve efficacy while limiting toxicity.

Background Incorporating backfill cohorts in phase I oncology trials is a recently developed strategy for dose optimization. However, the efficacy assessment window is long in general, causing a lag in identifying ineffective doses and more patients being backfilled to those doses. There is necessity to investigate how to use patient-reported outcomes (PRO) to determine doses for backfill cohorts. Methods We propose a unified Bayesian design framework, called ‘Backfill-QoL’, to utilize patient-reported quality of life (QoL) data into phase I oncology trials with backfill cohorts, including methods for trial monitoring, algorithm for dose-finding, and criteria for dose selection. Simulation studies and sensitivity analyses are conducted to evaluate the proposed Backfill-QoL design. Results The simulation studies demonstrate that the Backfill-QoL design is more efficient than traditional dose-expansion strategy, and fewer patients would be allocated to doses with unacceptable QoL profiles. A user-friendly Windows desktop application is developed and freely available for implementing the proposed design. Conclusions The Backfill-QoL design enables continuous monitoring of safety, efficacy and QoL outcomes, and the recommended phase II dose (RP2D) can be identified in a more patient-centered perspective.

Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that integrates the recent DL technologies and applications in drug discovery Including, drug–target interactions (DTIs), drug–drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions. We present a review of more than 300 articles between 2000 and 2022. The benchmark data sets, the databases, and the evaluation measures are also presented. In addition, this paper provides an overview of how explainable AI (XAI) supports drug discovery problems. The drug dosing optimization and success stories are discussed as well. Finally, digital twining (DT) and open issues are suggested as future research challenges for drug discovery problems. Challenges to be addressed, future research directions are identified, and an extensive bibliography is also included.

Background Cytotoxic chemotherapeutics form the cornerstone of systemic treatment of many cancers. Patients are dosed at maximum tolerated dose (MTD), which is carefully determined in phase I studies. In contrast, in murine studies, dosages are often based on customary practice or small pilot studies, which often are not well documented. Consequently, research groups need to replicate experiments, resulting in an excess use of animals and highly variable dosages across the literature. In addition, while patients often receive supportive treatments in order to allow dose escalation, mice do not. These issues could affect experimental results and hence clinical translation. Methods To address this, we determined the single-dose MTD in BALB/c and C57BL/6 mice for a range of chemotherapeutics covering the canonical classes, with clinical score and weight as endpoints. Results We found that there was some variation in MTDs between strains and the tolerability of repeated cycles of chemotherapy at MTD was drug-dependent. We also demonstrate that dexamethasone reduces chemotherapy-induced weight loss in mice. Conclusion These data form a resource for future studies using chemotherapy in mice, increasing comparability between studies, reducing the number of mice needed for dose optimisation experiments and potentially improving translation to the clinic.

In the face of competing first-line treatment options for CML, early prediction of prognosis on imatinib is desirable to assure favorable survival or otherwise consider the use of a second-generation tyrosine kinase inhibitor (TKI). A total of 1303 newly diagnosed imatinib-treated patients (pts) were investigated to correlate molecular and cytogenetic response at 3 and 6 months with progression-free and overall survival (PFS, OS). The persistence of BCR-ABL transcript levels >10% according to the international scale (BCR-ABL IS ) at 3 months separated a high-risk group (28% of pts; 5-year OS: 87%) from a group with >1–10% BCR-ABL IS (41% of pts; 5-year OS: 94%; P =0.012) and from a group with ⩽1% BCR-ABL IS (31% of pts; 5-year OS: 97%; P =0.004). Cytogenetics identified high-risk pts by >35% Philadelphia chromosome-positive metaphases (Ph+, 27% of pts; 5-year OS: 87%) compared with ⩽35% Ph+ (73% of pts; 5-year OS: 95%; P =0.036). At 6 months, >1% BCR-ABL IS (37% of pts; 5-year OS: 89%) was associated with inferior survival compared with ⩽1% (63% of pts; 5-year OS: 97%; P <0.001) and correspondingly >0% Ph+ (34% of pts; 5-year OS: 91%) compared with 0% Ph+ (66% of pts; 5-year OS: 97%; P =0.015). Treatment optimization is recommended for pts missing these landmarks.

Eftozanermin alfa (eftoza), a second-generation tumor necrosis factor-related apoptosis-inducing ligand receptor (TRAIL-R) agonist, induces apoptosis in tumor cells by activation of death receptors 4/5. This phase 1 dose-escalation/dose-optimization study evaluated the safety, pharmacokinetics, pharmacodynamics, and preliminary activity of eftoza in patients with advanced solid tumors. Patients received eftoza 2.5–15 mg/kg intravenously on day 1 or day 1/day 8 every 21 days in the dose-escalation phase, and 1.25–7.5 mg/kg once-weekly (QW) in the dose-optimization phase. Dose-limiting toxicities (DLTs) were evaluated during the first treatment cycle to determine the maximum tolerated dose (MTD) and recommended phase 2 dose (RP2D). Pharmacodynamic effects were evaluated in circulation and tumor tissue. A total of 105 patients were enrolled in the study (dose-escalation cohort, n = 57; dose-optimization cohort, n = 48 patients [n = 24, colorectal cancer (CRC); n = 24, pancreatic cancer (PaCA)]). In the dose-escalation cohort, seven patients experienced DLTs. MTD and RP2D were not determined. Most common treatment-related adverse events were increased alanine aminotransferase and aspartate aminotransferase levels, nausea, and fatigue. The one treatment-related death occurred due to respiratory failure. In the dose-optimization cohort, three patients (CRC, n = 2; PaCA, n = 1) had a partial response. Target engagement with regard to receptor saturation, and downstream apoptotic pathway activation in circulation and tumor were observed. Eftoza had acceptable safety, evidence of pharmacodynamic effects, and preliminary anticancer activity. The 7.5-mg/kg QW regimen was selected for future studies on the basis of safety findings, pharmacodynamic effects, and biomarker modulations. (Trial registration number: NCT03082209 (registered: March 17, 2017)).

An optimal antimicrobial dose provides enough drug to achieve a clinical response while minimizing toxicity and development of drug resistance. There can be considerable variability in pharmacokinetics, for example, owing to comorbidities or other medications, which affects antimicrobial pharmacodynamics and, thus, treatment success. Although current approaches to antimicrobial dose optimization address fixed variability, better methods to monitor and rapidly adjust antimicrobial dosing are required to understand and react to residual variability that occurs within and between individuals. We review current challenges to the wider implementation of antimicrobial dose optimization and highlight novel solutions, including biosensor-based, real-time therapeutic drug monitoring and computer-controlled, closed-loop control systems. Precision antimicrobial dosing promises to improve patient outcome and is important for antimicrobial stewardship and the prevention of antimicrobial resistance.There is considerable variability in antimicrobial pharmacokinetics and pharmacodynamics, which can pose challenges for treatment of infection and antimicrobial resistance development. In this Review, Holmes and colleagues discuss how precision antimicrobial therapy, including biosensors and individualized treatment, can contribute to antimicrobial stewardship.

We used advanced model-based methods to characterize the relationship between individual rifampicin exposure and antituberculosis treatment response. With data from a trial investigating high-dose rifampicin, a significant relation could be derived, and the clinical impact of increased doses was predicted. Abstract Background Tuberculosis remains a huge public health problem and the prolonged treatment duration obstructs effective tuberculosis control. Higher rifampicin doses have been associated with better bactericidal activity, but optimal dosing is uncertain. This analysis aimed to characterize the relationship between rifampicin plasma exposure and treatment response over 6 months in a recent study investigating the potential for treatment shortening with high-dose rifampicin. Methods Data were analyzed from 336 patients with pulmonary tuberculosis (97 with pharmacokinetic data) treated with rifampicin doses of 10, 20, or 35 mg/kg. The response measure was time to stable sputum culture conversion (TSCC). We derived individual exposure metrics with a previously developed population pharmacokinetic model of rifampicin. TSCC was modeled using a parametric time-to-event approach, and a sequential exposure-response analysis was performed. Results Higher rifampicin exposures increased the probability of early culture conversion. No maximal limit of the effect was detected within the observed range. The expected proportion of patients with stable culture conversion on liquid medium at week 8 was predicted to increase from 39% (95% confidence interval, 37%-41%) to 55% (49%-61%), with the rifampicin area under the curve increasing from 20 to 175 mg/L·h (representative for 10 and 35 mg/kg, respectively). Other predictors of TSCC were baseline bacterial load, proportion of culture results unavailable, and substitution of ethambutol for either moxifloxacin or SQ109. Conclusions Increasing rifampicin exposure shortened TSCC, and the effect did not plateau, indicating that doses >35 mg/kg could be yet more effective. Optimizing rifampicin dosage while preventing toxicity is a clinical priority.

Propionic acidaemia is a rare disorder caused by defects in the propionyl-coenzyme A carboxylase α or β (PCCA or PCCB) subunits that leads to an accumulation of toxic metabolites and to recurrent, life-threatening metabolic decompensation events. Here we report interim analyses of a first-in-human, phase 1/2, open-label, dose-optimization study and an extension study evaluating the safety and efficacy of mRNA-3927, a dual mRNA therapy encoding PCCA and PCCB. As of 31 May 2023, 16 participants were enrolled across 5 dose cohorts. Twelve of the 16 participants completed the dose-optimization study and enrolled in the extension study. A total of 346 intravenous doses of mRNA-3927 were administered over a total of 15.69 person-years of treatment. No dose-limiting toxicities occurred. Treatment-emergent adverse events were reported in 15 out of the 16 (93.8%) participants. Preliminary analysis suggests an increase in the exposure to mRNA-3927 with dose escalation, and a 70% reduction in the risk of metabolic decompensation events among 8 participants who reported them in the 12-month pretreatment period. Interim data from a clinical trial of mRNA-3927—an mRNA therapeutic for propionic acidaemia—provide early indications of the safety and efficacy of the treatment, and suggest that this approach might be applicable to other rare diseases.