When drugs enter systemic circulation, they interact with various components of the blood, including proteins such as human serum albumin (HSA), α₁-acid glycoprotein (AAG), lipoproteins, globulins, and red blood cells (RBCs).

HSA is the most abundant plasma protein and is vital in drug binding. It contains distinct drug-binding sites, with different drugs exhibiting affinity for specific sites. There are three main drug-binding domains for HSA: sites I, II, and III. These domains are further subdivided into two subgroups per site: IA, IB, IIA, IIB, IIIA, and IIIB. For example, warfarin, certain nonsteroidal anti-inflammatory drugs (NSAIDs), and sodium valproate bind to site I on HSA. Benzodiazepines, ibuprofen, and cloxacillin bind to site II. Diazepam and insulin detemir (Levemir) bind to site III. HSA also contains site IV, which tamoxifen binds to and is known as the tamoxifen binding site. Only a few drugs bind to sites III and IV.

AAG, another plasma protein, binds drugs, including imipramine, lidocaine, and propranolol. This protein can influence the distribution and pharmacokinetics of these drugs within the body.

Lipoproteins transport lipids in the bloodstream and can bind certain lipophilic drugs. Drugs with a high lipid content, such as cyclosporine and amiodarone, exhibit binding to lipoproteins. The extent of this binding can impact the drug's distribution and elimination.

Plasma globulins, including α₁-globulin, can bind steroids like cortisone and prednisone. This binding can influence the distribution and pharmacological effects of these drugs.

RBCs also play a role in drug interactions. Lipophilic drugs have a higher affinity for RBCs compared to hydrophilic drugs. Specific components such as hemoglobin, carbonic anhydrase, and cell membranes within RBCs can bind distinct drugs. For example, drugs like phenytoin bind to hemoglobin, while acetazolamide binds to carbonic anhydrase. Imipramine can bind to the RBC membrane.

Understanding these interactions between drugs and blood components is vital in pharmacology. It helps predict drug distribution, metabolism, and pharmacokinetic behavior within the body. Knowledge of these interactions aids in optimizing drug therapy, understanding potential drug-drug interactions, and ensuring effective and safe pharmacological outcomes.