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Purpose:Ceralasertib is a potent and selective oral inhibitor of the serine/threonine protein kinase ataxia telangiectasia and Rad3-related (ATR) protein.Patients and Methods:Eligible patients with solid tumors, enriched for melanoma, received ceralasertib in combination with a fixed dose of paclitaxel (80 mg/m2 on D1, D8, D15) in 28-day cycles. The dose of ceralasertib was escalated to reach an MTD in a rolling 6 design. The starting dose of ceralasertib was 40 mg QD. Fifty-seven patients (33 patients with melanoma who failed prior PD1/L1 treatment) were enrolled in 7 dose cohorts ranging from 40 mg QD to 240 mg BD plus weekly paclitaxel.Results:The RP2D was established as ceralasertib 240 mg BD days 1–14 plus paclitaxel 80 mg/m2 on D1, D8, D15 every 28 days. The most common toxicities were neutropenia (n = 39, 68%), anemia (n = 25, 44%), and thrombocytopenia (n = 21, 37%). In the full analysis set of 57 patients, the overall response rate (ORR) was 22.6% (95% CI, 12.5–35.3). In 33 patients with melanoma, resistant to prior anti-PD1 therapy, the ORR was 33.3% (95% CI, 18.0–51.8). In the melanoma subset, the mPFS was 3.6 months (95% CI, 2.0–5.8), the median duration of response was 9.9 months (95% CI, 3.7–23.2), and the mOS was 7.4 months (95% CI, 5.7–11.9).Conclusions:Ceralasertib in combination with paclitaxel was well tolerated in patients with advanced malignancies and showed evidence of antitumor activity. Durable responses were observed in patients with advanced cutaneous, acral, and mucosal melanoma resistant to anti-PD1/L1 treatment.See related commentary by Ashworth, p. 4667

Supplemental data for the main manuscript. Contains equation description of the main features of the framework, description of the pharmacokinetic models and parameter estimates used for the simulations of the treatment effects, additional model fitting results and synergy/antagornism assesments, analysis of different dosing schedules and four different demos of the R package.

Identification of optimal schedules for combination drug administration relies on accurately estimating the correct pharmacokinetics, pharmacodynamics, and drug interaction effects. Misspecification of pharmacokinetics can lead to wrongly predicted timing or order of treatments, leading to schedules recommended on the basis of incorrect assumptions about absorption and elimination of a drug and its effect on tumor growth. Here, we developed a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data. The software can be used to compare prespecified schedules on the basis of the number of resistant cells where drug interactions and pharmacokinetic curves can be estimated from user-provided data or models. We applied our approach to publicly available in vitro data of treatment with different tyrosine kinase inhibitors of BT-20 triple-negative breast cancer cells and of treatment with erlotinib of PC-9 non–small cell lung cancer cells. Our approach is publicly available in the form of an R package called ACESO (https://github.com/Michorlab/aceso) and can be used to investigate optimum dosing for any combination treatment.Significance:These findings introduce a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data.

Quick guide to model assumptions

Supplementary Table 1. Treatment Outcomes for individual trial participants and dose cohort (N=57)

Supplementary Figure 1. Single dose ceralasertib PK. PK from patients in cohorts 1 to 4 was in line with the PK model predictions (shadowed area) built with data from other studies. Supplementary Figure 2. ATM protein expression by IHC in patients with melanoma. Supplementary Figure 3. ctDNA clearance of mutant alleles from a responder to ceralasertib+paclitaxel; example 1. ctDNA was sequenced at baseline, day 61 and day 403. Re-staging scans were performed at day 63, day 117, intermediate days at approximate 8 week intervals (not shown) and day 426 (at the time of PD). The stream plot shows rapid clearance of the mutant alleles in line with RECIST v1.1 response. Supplementary Figure 4. CtDNA clearance of mutant alleles from a responder to ceralasertib+paclitaxel; example 2. ctDNA was sequenced at baseline, day 24, day 52 and day 241. Re-staging scans were performed at day 46, day 98, intermediate days at approximate 8 week intervals (not shown) and day 235. The stream plot shows rapid clearance of the mutant alleles prior to the day of the first re-staging scan which showed a RECIST v1.1 response.

Background: SERENA-1 (NCT03616587) is a Phase 1, multi-part, open-label study of camizestrant in women with ER+/HER2− advanced breast cancer. Parts A/B and C/D (escalation/expansion) examined camizestrant as monotherapy and in combination with palbociclib respectively and have been presented previously.1,2 Here we present data from parts G/H which examined camizestrant in combination with abemaciclib. Methods: The primary objective was to determine the safety and tolerability of camizestrant 75 mg once daily (QD) in combination with abemaciclib 150 mg twice daily (BID). Secondary objectives included investigation of anti-tumor response and pharmacokinetics (PK). Participants were previously treated women of any menopausal status (pre-menopausal women received this combination alongside ongoing ovarian function suppressors). Prior treatment with ≤2 lines of chemotherapy in the advanced setting was permitted. There was no limit on the number of lines of prior endocrine treatment in the advanced setting; previous treatment with CDK4/6 inhibitors (CDK4/6i) and fulvestrant was permitted. Results: As of 1st June 2022, 24 patients had received camizestrant in combination with abemaciclib with a median 7.7 month follow up. Tolerability of the combination of camizestrant and abemaciclib was consistent with that of each drug individually. No patient required camizestrant dose reduction. All camizestrant-related heart rate decreases were Grade 1 (asymptomatic). PK data for camizestrant in combination with abemaciclib were consistent with camizestrant as monotherapy and published abemaciclib steady-state PK data, indicating no clinically relevant drug-drug interaction. In these heavily pre-treated patients (46% prior chemotherapy, 75% prior CDK4/6i, 54% prior fulvestrant; all in the advanced disease setting) and of whom 67% had visceral metastases, the objective response rate was 5/19 (26.3%), the clinical benefit rate at 24 weeks was 16/24 (66.7%) and the median progression-free survival had not been reached, with 8/24 patients experiencing a progression event. These data support the use of camizestrant 75 mg QD combined with the approved abemaciclib dose. Conclusions: Camizestrant 75 mg QD in combination with abemaciclib 150 mg BID was well tolerated with encouraging clinical activity. The inclusion of this regimen in the ongoing Phase 3, SERENA-6 trial 3, of camizestrant combined with CDK4/6i versus an aromatase inhibitor, will further clarify the role of this combination in the treatment of patients with ER+/HER2− advanced breast cancer with tumors expressing ESR1 mutations. References 1. Baird R, Oliveira M, Ciruelos Gil EM, et al. SABCS 2020 Virtual Meeting. Abstract PS11-05. 2. Oliveira M, Hamilton EP, Incorvati J, et al. J Clin Oncol 40, 2022 (suppl 16; abstr 1032). 3. SERENA-6 trial. Available at https://clinicaltrials.gov/ct2/show/NCT04964934 We acknowledge Helen Heffron, PhD, from InterComm International who provided medical writing support funded by AstraZeneca. Citation Format: Nicholas Turner, Christos Vaklavas, Emiliano Calvo, Javier Garcia-Corbacho, Jason Incorvati, Manuel Ruiz Borrego, Chris Twelves, Anne Armstrong, Begoña Bermejo, Erika Hamilton, Mafalda Oliveira, Eva Ciruelos, Peter Kabos, Manish R Patel, Maria Borrell, Howard Burris, Bruno de Paula, Alejandro Falcon, Cristina Hernando, Irene Moreno, Ciara S. O’Brien, Elena Shagisultanova, Ivan Victoria Ruiz, Judy S. Wang, Mei Wei, Tim Brier, Danielle Carroll, Carmela Ciardullo, Lisa Gibbons, itziar irurzun-Arana, Tony Jack, bistra kirova, Teresa Klinowska, Justin Lindemann, Julie Maidment, Alastair Mathewson, Rhiannon Maudsley, Robert McEwen, Christopher Morrow, Andy Sykes, Richard D. Baird. SERENA-1: Updated analyses from a Phase 1 study of the next generation oral selective estrogen receptor degrader camizestrant (AZD9833) combined with abemaciclib, in women with ER-positive, HER2-negative advanced breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-07-28.

Purpose: Ceralasertib is a potent and selective oral inhibitor of the serine/threonine protein kinase ataxia telangiectasia and Rad3-related (ATR) protein. Patients and Methods: Eligible patients with solid tumors, enriched for melanoma, received ceralasertib in combination with a fixed dose of paclitaxel (80 mg/m2 on D1, D8, D15) in 28-day cycles. The dose of ceralasertib was escalated to reach an MTD in a rolling 6 design. The starting dose of ceralasertib was 40 mg QD. Fifty-seven patients (33 patients with melanoma who failed prior PD1/L1 treatment) were enrolled in 7 dose cohorts ranging from 40 mg QD to 240 mg BD plus weekly paclitaxel. Results: The RP2D was established as ceralasertib 240 mg BD days 1–14 plus paclitaxel 80 mg/m2 on D1, D8, D15 every 28 days. The most common toxicities were neutropenia (n = 39, 68%), anemia (n = 25, 44%), and thrombocytopenia (n = 21, 37%). In the full analysis set of 57 patients, the overall response rate (ORR) was 22.6% (95% CI, 12.5–35.3). In 33 patients with melanoma, resistant to prior anti-PD1 therapy, the ORR was 33.3% (95% CI, 18.0–51.8). In the melanoma subset, the mPFS was 3.6 months (95% CI, 2.0–5.8), the median duration of response was 9.9 months (95% CI, 3.7–23.2), and the mOS was 7.4 months (95% CI, 5.7–11.9). Conclusions: Ceralasertib in combination with paclitaxel was well tolerated in patients with advanced malignancies and showed evidence of antitumor activity. Durable responses were observed in patients with advanced cutaneous, acral, and mucosal melanoma resistant to anti-PD1/L1 treatment. See related commentary by Ashworth, p. 4667

Intraoperative targeting of the analgesic effect still lacks an optimal solution. Opioids are currently the main drug used to achieve antinociception, and although underdosing can lead to an increased stress response, overdose can also lead to undesirable adverse effects. To better understand how to achieve the optimal analgesic effect of opioids, we studied the influence of remifentanil on the pupillary reflex dilation (PRD) and its relationship with the reflex movement response to a standardized noxious stimulus. The main objective was to generate population pharmacodynamic models relating remifentanil predicted concentrations to movement and to pupillary dilation during general anesthesia. A total of 78 patients undergoing gynecological surgery under general anesthesia were recruited for the study. PRD and movement response to a tetanic stimulus were measured multiple times before and after surgery. We used nonlinear mixed effects modeling to generate a population pharmacodynamic model to describe both the time profiles of PRD and movement responses to noxious stimulation. Our model demonstrated that movement and PRD are equally depressed by remifentanil. Using the developed model, we changed the intensity of stimulation and simulated remifentanil predicted concentrations maximizing the probability of absence of movement response. An estimated effect site concentration of 2ng/ml of remifentanil was found to inhibit movement to a tetanic stimulation with a probability of 81%.

The model-informed drug discovery and development paradigm is now well established among the pharmaceutical industry and regulatory agencies. This success has been mainly due to the ability of pharmacometrics to bring together different modeling strategies, such as population pharmacokinetics/pharmacodynamics (PK/PD) and systems biology/pharmacology. However, there are promising quantitative approaches that are still seldom used by pharmacometricians and that deserve consideration. One such case is the stochastic modeling approach, which can be important when modeling small populations because random events can have a huge impact on these systems. In this review, we aim to raise awareness of stochastic models and how to combine them with existing modeling techniques, with the ultimate goal of making future drug–disease models more versatile and realistic., Highlights Nonlinear mixed-effect modeling is currently the most successful methodology used to characterize the PK/PD data from different individuals. Stochastic modeling approaches deserve consideration for use in model-informed drug discovery and development because events occurring at random can have important repercussions on disease progression and treatment effects, especially when population size is small. Stochastic models can also help in the refinement of the original structural PK/PD model. Deterministic systems have a greater mathematical simplicity, are computationally less demanding, and parameter estimation is well established with a large existing toolkit to help with model fitting and simulation. Coronavirus disease 2019 is the most recent application where researchers are building such models to describe the spread of this disease in the population.

Advanced melanoma remains a disease with poor prognosis. Several serologic markers have been investigated to help monitoring and prognostication, but to date only lactate dehydrogenase (LDH) has been validated as a standard prognostic factor biomarker for this disease by the American Joint Committee on Cancer. In this work, we built a semi-mechanistic model to explore the relationship between the time course of several circulating biomarkers and overall or progression free survival in advanced melanoma patients treated with adjuvant high-dose interferon-$${\boldsymbol{\alpha }}{\bf{2}}{\bf{b}}$$α2b. Additionally, due to the adverse interferon tolerability, a semi-mechanistic model describing the side effects of the treatment in the absolute neutrophil counts is proposed in order to simultaneously analyze the benefits and toxic effects of this treatment. The results of our analysis suggest that the relative change from baseline of LDH was the most significant predictor of the overall survival of the patients. Unfortunately, there was no significant difference in the proportion of patients with elevated serum biomarkers between the patients who recurred and those who remained free of disease. Still, we believe that the modelling framework presented in this work of circulating biomarkers and adverse effects could constitute an additional strategy for disease monitoring in advance melanoma patients.

Purpose: This study reports the safety, tolerability, MTD, recommended phase II dose (RP2D), pharmacokinetic/pharmacodynamic profile, and preliminary antitumor activity of ceralasertib combined with carboplatin in patients with advanced solid tumors. It also examined exploratory predictive and pharmacodynamic biomarkers. Patients and Methods: Eligible patients (n = 36) received a fixed dose of carboplatin (AUC5) with escalating doses of ceralasertib (20 mg twice daily to 60 mg once daily) in 21-day cycles. Sequential and concurrent combination dosing schedules were assessed. Results: Two ceralasertib MTD dose schedules, 20 mg twice daily on days 4–13 and 40 mg once daily on days 1–2, were tolerated with carboplatin AUC5; the latter was declared the RP2D. The most common treatment-emergent adverse events (Common Terminology Criteria for Adverse Events grade ≥3) were anemia (39%), thrombocytopenia (36%), and neutropenia (25%). Dose-limiting toxicities of grade 4 thrombocytopenia (n = 2; including one grade 4 platelet count decreased) and a combination of grade 4 thrombocytopenia and grade 3 neutropenia occurred in 3 patients. Ceralasertib was quickly absorbed (tmax ∼1 hour), with a terminal plasma half-life of 8–11 hours. Upregulation of pRAD50, indicative of ataxia telangiectasia mutated (ATM) activation, was observed in tumor biopsies during ceralasertib treatment. Two patients with absent or low ATM or SLFN11 protein expression achieved confirmed RECIST v1.1 partial responses. Eighteen of 34 (53%) response-evaluable patients had RECIST v1.1 stable disease. Conclusions: The RP2D for ceralasertib plus carboplatin was established as ceralasertib 40 mg once daily on days 1–2 administered with carboplatin AUC5 every 3 weeks, with pharmacokinetic and pharmacodynamic studies confirming pharmacodynamic modulation and preliminary evidence of antitumor activity observed.

BackgroundTargeting the DNA damage repair (DDR) pathways is an attractive strategy for boosting cancer immunotherapy. Ceralasertib (AZD6738) is an oral kinase inhibitor of ataxia telangiectasia and Rad3 related protein, which is a master regulator of DDR. We conducted a phase II trial of ceralasertib plus durvalumab in patients with previously treated advanced gastric cancer (AGC) to demonstrate the safety, tolerability, and clinical activity of the combination.MethodsThis phase II, open-label, single-center, non-randomized study was designed to evaluate the efficacy and safety of ceralasertib in combination with durvalumab in patients with AGC. The study drug regimen was ceralasertib (240 mg two times a day) days 15–28 in a 28-day cycle in combination with durvalumab (1500 mg) at day 1 every 4 weeks. The primary end point was overall response rate (ORR) by Response Evaluation Criteria in Solid Tumors (V.1.1). Exploratory biomarker analysis was performed using fresh tumor biopsies in all enrolled patients.ResultsAmong 31 patients, the ORR, disease control rate, median progression-free survival (PFS), and overall survival were 22.6% (95% CI 9.6% to 41.1%), 58.1% (95% CI 39.1% to 75.5%), 3.0 (95% CI 2.1 to 3.9) months, and 6.7 (95% CI 3.8 to 9.6) months, respectively. Common adverse events were manageable with dose modification. A subgroup of patients with a loss of ataxia telangiectasia mutated (ATM) expression and/or high proportion of mutational signature attributable to homologous repair deficiency (sig. HRD) demonstrated a significantly longer PFS than those with intact ATM and low sig. HRD (5.60 vs 1.65 months; HR 0.13, 95% CI 0.045 to 0.39; long-rank pConclusionsCeralasertib plus durvalumab has promising antitumor activity, with durable responses in patients with refractory AGC. Thus, a biomarker-driven trial is required.Trial registrationNCT03780608.

1032 Background: SERENA-1 (NCT03616587) is a Phase 1, multi-part, open-label study of camizestrant in women with ER+, HER2− advanced breast cancer (ABC). Parts A/B (escalation/expansion) assessed camizestrant monotherapy and have been presented previously. Parts C/D examine camizestrant in combination with palbociclib; here we present mature data from camizestrant 75 mg in combination with palbociclib; 75 mg being the camizestrant dose currently under investigation in the Phase 3 studies SERENA-4 (NCT04711252) and SERENA-6 (NCT04964934). Methods: The primary objective was to determine the safety and tolerability of camizestrant once daily (QD) with palbociclib. Secondary objectives included anti-tumor response and pharmacokinetics (PK). Prior treatment with < 2 lines of chemotherapy in the advanced setting was permitted. There was no limit on the number of lines of prior endocrine treatment in the advanced setting; prior treatment with CDK4/6 inhibitors and fulvestrant (F) was permitted. Results: As of 9 September 2021, 25 patients had received camizestrant 75 mg QD in combination with palbociclib. Tolerability of the combination of camizestrant 75 mg and palbociclib was consistent with that of each drug individually. No patient required camizestrant dose interruption/reduction/discontinuation due to a camizestrant-related adverse event (AE); moreover, none experienced a Grade ≥3 camizestrant-related AE. All camizestrant-related heart rate reductions were Grade 1 (asymptomatic). All camizestrant-related visual effects were Grade 1 (mild), apart from one patient who experienced transient Grade 2 (moderate) visual effects that resolved to Grade 1 without dose modification. Camizestrant-related gastrointestinal disorders were all Grade 1, except one instance of Grade 2 nausea lasting one day. PK data for camizestrant 75 mg QD and palbociclib combined were broadly consistent with camizestrant as monotherapy and published palbociclib steady-state PK data, further supporting the use of camizestrant 75 mg QD (Phase 3 dose) in combination with the approved palbociclib doses. In these heavily pre-treated patients (48% prior chemotherapy, 80% prior CDK4/6i, 68% prior F; all in advanced disease setting) and of whom 60% had visceral metastases, the clinical benefit rate at 24 weeks was 7/25 (28%). Conclusions: The PK and safety profile of camizestrant 75 mg QD in combination with palbociclib is favorable in this mature Phase 1 dataset. Despite extensive pre-treatment - including chemotherapies, CDK4/6i, and F - camizestrant 75 mg QD in combination with palbociclib exhibits encouraging clinical activity. The results from the ongoing Phase 3 studies, SERENA-4 and SERENA-6, will further elucidate the role of this combination in the treatment of patients with HR+/HER2− ABC. Clinical trial information: NCT03616587.

Many drugs that have been proposed for treatment of coronavirus disease 2019 (COVID-19) are reported to cause cardiac adverse events, including ventricular arrhythmias. In order to properly weigh risks against potential benefits, particularly when decisions must be made quickly, mathematical modeling of both drug disposition and drug action can be useful for predicting patient response and making informed decisions. Here, we explored the potential effects on cardiac electrophysiology of four drugs proposed to treat COVID-19: lopinavir, ritonavir, chloroquine, and azithromycin, as well as combination therapy involving these drugs. Our study combined simulations of pharmacokinetics (PKs) with quantitative systems pharmacology (QSP) modeling of ventricular myocytes to predict potential cardiac adverse events caused by these treatments. Simulation results predicted that drug combinations can lead to greater cellular action potential prolongation, analogous to QT prolongation, compared with drugs given in isolation. The combination effect can result from both PK and pharmacodynamic drug interactions. Importantly, simulations of different patient groups predicted that women with pre-existing heart disease are especially susceptible to drug-induced arrhythmias, compared with diseased men or healthy individuals of either sex. Statistical analysis of population simulations revealed the molecular factors that make certain women with heart failure especially susceptible to arrhythmias. Overall, the results illustrate how PK and QSP modeling may be combined to more precisely predict cardiac risks of COVID-19 therapies.

Identification of optimal schedules for combination drug administration relies on accurately estimating the correct pharmacokinetics, pharmacodynamics, and drug interaction effects. Misspecification of pharmacokinetics can lead to wrongly predicted timing or order of treatments, leading to schedules recommended on the basis of incorrect assumptions about absorption and elimination of a drug and its effect on tumor growth. Here, we developed a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data. The software can be used to compare prespecified schedules on the basis of the number of resistant cells where drug interactions and pharmacokinetic curves can be estimated from user-provided data or models. We applied our approach to publicly available in vitro data of treatment with different tyrosine kinase inhibitors of BT-20 triple-negative breast cancer cells and of treatment with erlotinib of PC-9 non–small cell lung cancer cells. Our approach is publicly available in the form of an R package called ACESO (https://github.com/Michorlab/aceso) and can be used to investigate optimum dosing for any combination treatment. Significance: These findings introduce a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data.

Drug development in Systemic Lupus Erythematosus (SLE) has been hindered by poor translation from successful preclinical experiments to clinical efficacy. This lack of success has been attributed to the high heterogeneity of SLE patients and to the lack of understanding of disease physiopathology. Modelling approaches could be useful for supporting the identification of targets, biomarkers and patient subpopulations with differential response to drugs. However, the use of traditional quantitative models based on differential equations is not justifiable in a sparse data situation. Boolean networks models are less demanding on the required data to be implemented and can provide insights into the dynamics of biological networks. This methodology allows the integration of all the available knowledge into a single framework to evaluate the behavior of the system under different conditions and test hypotheses about unknown aspects of the disease. In this proof-of-concept study, we explored the potential of a systems pharmacology model based on Boolean networks to support drug development in SLE. We focused the analysis on the antigen presentation by the antigen presenting cells (APC) to the T-cells to evaluate the scope of this methodology in a medium size network before full implementation of the whole SLE pathway. The heterogeneity of SLE patients was replicated using this methodology simulating subjects with distinct pathway alterations. A perturbation analysis of the network coupled with clustering analysis showed potential to identify drug targets, optimal combinatorial regimens and subpopulations of responders and non-responders to drug treatment. We propose this approach as a first step towards the development of more quantitative platforms to address the current challenges in drug development for complex diseases.

Numerous problems encountered in computational biology can be formulated as optimization problems. In this context, optimization of drug release characteristics or dosing schedules for anticancer agents has become a prominent area not only for the development of new drugs, but also for established drugs. However, in complex systems, optimization of drug exposure is not a trivial task and cannot be efficiently addressed through trial-error simulation exercises. Finding a solution to those problems is a challenging task which requires more advanced strategies like optimal control theory. In this work, we perform an optimal control analysis on a previously developed computational model for the testosterone effects of triptorelin in prostate cancer patients with the goal of finding optimal drug-release characteristics. We demonstrate how numerical control optimization of non-linear models can be used to find better therapeutic approaches in order to improve the final outcome of the patients., Author summary Mathematical models of the disease processes are widely used in computational biology to quantitatively describe the time course of disease progression and are often linked to pharmacokinetic–pharmacodynamic models in order to evaluate the effect of drug treatment on disease. Once the models are built from observed information and/or literature data, they can predict the dynamics of the system under different conditions through computer simulations. However, simulation exercises are not always effective to obtain the desired objectives due to the complexity of these systems. In this work, we optimized the release characteristics of a synthetic gonadotropin-releasing hormone analog used to induce chemical castration by inhibiting the testosterone levels in prostate cancer patients. The therapeutic goals to achieve were to minimize the initial flare up of testosterone levels and the time to reach testosterone values below castration limit, while maximizing the castration period of the patients. Our methodology, based on control theory, introduces a manipulable variable into the system’s equations to drive the model towards the established goals. We demonstrated how drug-release properties can be improved with the implementation of optimal control strategies to enhance the outcome of cancer patients. These methods are extrapolable to other problems encountered in the field.

Motivation : The literature on complex diseases is abundant but not always quantitative. This is particularly so for Inflammatory Bowel Disease (IBD), where many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. We propose the elaboration and validation of a logic network for IBD able to capture the information available in the literature that will facilitate the identification/validation of therapeutic targets. Results : In this article, we propose a logic model for Inflammatory Bowel Disease (IBD) which consists of 43 nodes and 298 qualitative interactions. The model presented is able to describe the pathogenic mechanisms of the disorder and qualitatively describes the characteristic chronic inflammation. A perturbation analysis performed on the IBD network indicates that the model is robust. Also, as described in clinical trials, a simulation of anti-TNF alpha, anti-IL2 and Granulocyte and Monocyte Apheresis showed a decrease in the Metalloproteinases node (MMPs), which means a decrease in tissue damage. In contrast, as clinical trials have demonstrated, a simulation of anti-IL17 and anti-IFN gamma or IL10 overexpression therapy did not show any major change in MMPs expression, as corresponds to a failed therapy. The model proved to be a promising in silico tool for the evaluation of potential therapeutic targets, the identification of new IBD biomarkers, the integration of IBD polymorphisms to anticipate responders and non-responders and can be reduced and transformed in quantitative model/s.

Motivation Literature on complex diseases is abundant but not always quantitative. Many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. Tools for analysis of discrete networks are useful to capture the available information in the literature but have not been efficiently integrated by the pharmaceutical industry. We propose an expansion of the usual analysis of discrete networks that facilitates the identification/validation of therapeutic targets. Results In this article, we propose a methodology to perform Boolean modeling of Systems Biology/Pharmacology networks by using SPIDDOR (Systems Pharmacology for effIcient Drug Development On R) R package. The resulting models can be used to analyze the dynamics of signaling networks associated to diseases to predict the pathogenesis mechanisms and identify potential therapeutic targets. Availability and Implementation The source code is available at https://github.com/SPIDDOR/SPIDDOR. Supplementary information Supplementary data are available at Bioinformatics online.