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Pharmaceutics
Special Issues
Drug–Drug Interactions—New Perspectives
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Drug–Drug Interactions—New Perspectives

A special issue of Pharmaceutics (ISSN 1999-4923). This special issue belongs to the section "Pharmacokinetics and Pharmacodynamics".

Deadline for manuscript submissions: 10 May 2026 | Viewed by 1412

Special Issue Editors

Dr. Andrzej Czyrski

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Guest Editor
Department of Physical Pharmacy and Pharmacokinetics, Poznań University of Medical Sciences, 60-781 Poznan, Poland
Interests: drug analysis; pharmacokinetics; aritficial neural networks; data mining; drug–drug interactions
Special Issues, Collections and Topics in MDPI journals
Dr. Matylda Resztak

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Guest Editor
Department of Physical Pharmacy and Pharmacokinetics, Poznań University of Medical Sciences, Poznan, Poland
Interests: therapeutic drug monitoring; HPLC; method validation; polymorphism; pharmacokinetics
Special Issues, Collections and Topics in MDPI journals
Dr. Joanna Sobiak

E-Mail Website
Guest Editor
Department of Physical Pharmacy and Pharmacokinetics, Poznań University of Medical Sciences, Poznan, Poland
Interests: therapeutic drug monitoring; pediatrics; analytical methods; population pharmacokinetics; drug–drug interactions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Drug–drug interactions (DDI) represent a significant challenge in healthcare, arising when two or more substances, including pharmaceuticals and dietary supplements, interact with one another. These interactions can profoundly affect the pharmacokinetics and pharmacodynamics of the involved drugs, potentially diminishing therapeutic efficacy and increasing the risk of adverse effects.

This Special Issue invites research that examines DDIs, particularly those resulting from co-medication. We are particularly interested in studies that investigate the pharmacokinetic profiles altered by these interactions, as well as the resultant impacts on drug metabolism, distribution, and excretion. Manuscripts discussing side effects that emerge from medical procedures are also encouraged.

Research focusing on specific patient populations, especially pediatric and elderly patients, is of prime interest due to their distinct pharmacokinetic characteristics that may heighten vulnerability to DDIs.

We encourage the application of advanced analytical methods, including statistical modeling and machine learning techniques, for evaluating DDIs. However, submissions employing these models must include supporting biological experiments to confirm findings and ensure their relevance within the scope of pharmaceutics and pharmacokinetics.

Dr. Andrzej Czyrski
Dr. Matylda Resztak
Dr. Joanna Sobiak
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceutics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • safety of therapy
  • cytochrome P450
  • pharmacokinetics
  • contraindication
  • machine learning
  • metabolism

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Published Papers (2 papers)

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Research

23 pages, 2072 KB  
Article
Sexual Function and Depressive Symptoms in Metformin-Treated Women with Drug-Induced Hyperprolactinemia and Different Vitamin D Status: A Pilot Study
by Robert Krysiak, Witold Szkróbka, Karolina Kowalcze and Bogusław Okopień
Pharmaceutics 2026, 18(3), 376; https://doi.org/10.3390/pharmaceutics18030376 - 18 Mar 2026
Viewed by 299
Abstract
Background: Elevated prolactin levels are associated with disturbances in female sexual function. While long-term therapy with dopamine agonists has been shown to improve these disturbances, the therapeutic benefits appear to be reduced in the presence of vitamin D deficiency or insufficiency. Therefore, the [...] Read more.
Background: Elevated prolactin levels are associated with disturbances in female sexual function. While long-term therapy with dopamine agonists has been shown to improve these disturbances, the therapeutic benefits appear to be reduced in the presence of vitamin D deficiency or insufficiency. Therefore, the present study aimed to examine whether vitamin D status modulates the effects of metformin—a medication with less pronounced prolactin-lowering properties—on sexual function and depressive symptoms. Methods: The study cohort comprised three groups of reproductive-age women with drug-induced hyperprolactinemia and prediabetes, matched for age, glycated hemoglobin, and prolactin concentrations. Group I included 25 women with normal vitamin D status who were not receiving vitamin D supplementation. Group II consisted of 25 women with vitamin D deficiency or insufficiency that was adequately corrected through supplementation, while group III included 25 women with untreated vitamin D deficiency or insufficiency. All participants received metformin throughout the six-month study period. Female sexual function and depressive symptoms were assessed before and after metformin therapy using the Female Sexual Function Index (FSFI) and the Beck Depression Inventory-II (BDI-II), respectively. Additional outcome measures included plasma 25-hydroxyvitamin D, fasting plasma glucose, glycated hemoglobin (HbA1c), the homeostatic model assessment of insulin resistance (HOMA-IR), prolactin, gonadotropins, and sex hormones. Results: Improvements in glucose homeostasis were observed across all groups; however, these changes were more pronounced in groups I and II than in group III. Reductions in prolactin concentrations (total and monomeric), accompanied by increases in gonadotropins, estradiol, and testosterone, were observed exclusively in women with normal vitamin D status. In groups I and II, metformin therapy resulted in significant improvements in total FSFI scores as well as in all individual domain scores. In contrast, in group III, the effects of metformin were limited to increases in the domain scores for lubrication and sexual satisfaction. Improvements in sexual function were positively associated with baseline 25-hydroxyvitamin D levels, reductions in prolactin concentrations, and, to a lesser extent, treatment-related changes in HbA1c and increases in testosterone. A treatment-induced reduction in total BDI-II scores was observed only among women with normal vitamin D status. Conclusions: Low vitamin D status diminishes the beneficial effects of metformin on sexual function and depressive symptoms in reproductive-age women with iatrogenic hyperprolactinemia. Full article
(This article belongs to the Special Issue Drug–Drug Interactions—New Perspectives)
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22 pages, 586 KB  
Article
Onco-Hem Connectome—Network-Based Phenotyping of Polypharmacy and Drug–Drug Interactions in Onco-Hematological Inpatients
by Sabina-Oana Vasii, Daiana Colibășanu, Florina-Diana Goldiș, Sebastian-Mihai Ardelean, Mihai Udrescu, Dan Iliescu, Daniel-Claudiu Malița, Ioana Ioniță and Lucreția Udrescu
Pharmaceutics 2026, 18(2), 146; https://doi.org/10.3390/pharmaceutics18020146 - 23 Jan 2026
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Abstract
We introduce the Onco-Hem Connectome (OHC), a patient similarity network (PSN) designed to organize real-world hemato-oncology inpatients by exploratory phenotypes with potential clinical utility. Background: Polypharmacy and drug–drug interactions (DDIs) are pervasive in hemato-oncology and vary with comorbidity and treatment intensity. Methods: We [...] Read more.
We introduce the Onco-Hem Connectome (OHC), a patient similarity network (PSN) designed to organize real-world hemato-oncology inpatients by exploratory phenotypes with potential clinical utility. Background: Polypharmacy and drug–drug interactions (DDIs) are pervasive in hemato-oncology and vary with comorbidity and treatment intensity. Methods: We retrospectively analyzed a 2023 single-center cohort of 298 patients (1158 hospital episodes). Standardized feature vectors combined demographics, comorbidity (Charlson, Elixhauser), comorbidity polypharmacy score (CPS), aggregate DDI severity score (ADSS), diagnoses, and drug exposures. Cosine similarity defined edges (threshold ≥ 0.6) to build an undirected PSN; communities were detected with modularity-based clustering and profiled by drugs, diagnosis codes, and canonical chemotherapy regimens. Results: The OHC comprised 295 nodes and 4179 edges (density 0.096, modularity Q = 0.433), yielding five communities. Communities differed in comorbidity burden (Kruskal–Wallis ε2: Charlson 0.428, Elixhauser 0.650, age 0.125, all FDR-adjusted p < 0.001) but not in utilization (LOS, episodes) after FDR (ε2 ≈ 0.006–0.010). Drug enrichment (e.g., enoxaparin Δ = +0.13 in Community 2; vinblastine Δ = +0.09 in Community 3) and principal diagnoses (e.g., C90.0 23%, C91.1 15%, C83.3 15% in Community 1) supported distinct clinical phenotypes. Robustness analyses showed block-equalized features preserved communities (ARI 0.946; NMI 0.941). Community drug signatures and regimen signals aligned with diagnosis patterns, reflecting the integration of resource-use variables in the feature design. Conclusions: The Onco-Hem Connectome yields interpretable, phenotype-level insights that can inform supportive care bundles, DDI-aware prescribing, and stewardship, and it provides a foundation for phenotype-specific risk models (e.g., prolonged stay, infection, high-DDI episodes) in hemato-oncology. Full article
(This article belongs to the Special Issue Drug–Drug Interactions—New Perspectives)
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